Reviewerly/exHarmony huggingface dataset:
Improving the Efficiency of Gabriel Graph-based Classifiers for Hardware-optimized Implementations

This work evaluates strategies to reduce the computational cost of Gabriel graph-based classifiers, in order to make them more suitable for hardware implementation. An analysis of the impact of the bit precision provides insights on the model's robustness for lower precision arithmetic. Additionally, a parallelization technique is proposed in order to improve the efficiency of the support edges computation. The results show that the lower bit precision models are statistically equivalent to the reference double-precision ones. Also, the implementation of the proposed parallel algorithm provides a significant reduction in the running time when applied in large datasets, while maintaining its accuracy.
Unsupervised GIST based Clustering for Object Localization

In the past years, there have been several attempts for the task of object localization in an image. However, most of the algorithms for object localization have been either supervised or weakly supervised. The work presented in this paper is based on the localization of a single object instance, in an image, in a fully unsupervised manner. Initially, from the input image, object proposals are generated where the proposal score for each of these proposals is calculated using a saliency map. Next, a graph by the GIST feature similarity between each pair of proposals is constructed. Density-based spatial clustering of applications with noise (DBSCAN) is used to make clusters of proposals based on GIST similarity, which eventually helps us in the final localization of the object. The setup is evaluated on two challenging benchmark datasets - PASCAL VOC 2007 dataset and object discovery dataset. The performance of the proposed approach is observed to be comparable with various state-of-the-art weakly supervised and unsupervised approaches for the problem of localization of an object.
A 3D Vision Tracking Method for Mechanism Validation

3D tracking of mechanisms in a non-structured environment is a very challenging task. It is important for the development of robotic applications, since key points can be tracked and used to control or to validate the system. In this paper, a 3D object-tracking method is presented. The proposed method allows to track a marker or a specific point also in poorly-lighted environments through images captured by a camera in combination with a depth sensor, thus obtaining the 3D Point Cloud of the entity as result. In addition, the velocity and the trajectory of the marker can be obtained. Two different acquisitions are reported as example, one for a cable-driven rehabilitation device and one for a lower-mobility parallel mechanism.
Co-Adapt

While software tools can be very powerful, a one-size-fits-all approach does not work because individual needs vary over time. Co-Adapt is a software framework that learns from user activity and adjusts the interface in real time to suit changing needs. Prior research has shown that the best in class user interfaces (UI) are not as effective across multiple groups of people. We performed three experiments by integrating with three distinct UI prototypes that tested iteratively tailored software to show the effectiveness of different UI presentations. Co-Adapt demonstrates that a multitude of UIs improve usability, which in some instances lead to greater tool adoption. Our success across three domains suggests generalizability of the framework and is promising for further experimentation across other application areas.
“Always the same unrememberable revelation”: Thoreau’s Telegraph Harp, the Development of an Immanent Romantic Secularism, and Golden Age Children’s Literature

Courtney A. Floyd, “‘Always the same unrememberable revelation’: Thoreau’s Telegraph Harp, the Development of an Immanent Romantic Secularism, and Golden Age Children’s Literature” (pp. 30–53) In this essay, I analyze Henry David Thoreau’s references to and use of the telegraph, Aeolian harp, and telegraph harp in his journals between the years 1851 and 1853, A Week on the Concord and Merrimack Rivers (1849), and Walden (1854). I argue that the figure of the telegraph harp, though only briefly mentioned in Thoreau’s published works, represents a significant reworking of Romantic sensibility into a modern, immanent, and secular narrative of mundane transcendence that undergirds his particular transcendentalism. Thoreau obfuscates the material modernity of the telegraph harp even as he rescripts its very materiality. His diction in passages about the telegraph harp implies a sort of transcendence but grounds that transcendence in the natural landscape and a physical symbol of modern technology, establishing a conceptual legacy that informs not only second-generation transcendentalism, but also and unexpectedly, Golden Age children’s literature of the late nineteenth and early twentieth centuries.
Significant Target Detection of Traffic Signs Based on Walsh-Hadamard Transform

This paper proposes a method based on Walsh-Hadamard transform to detect the significant target of traffic signs. This method uses the Walsh-Hadamard transform and normalized image binary spectrum to detect the traffic target with significant target. Experiments show that the algorithm is shorter than other algorithms and can quickly and effectively detect the significant area of traffic signs compared with other algorithms in the text.
Genetic Algorithm as a Solutions Optimization of Tilt Angles for Monthly Periods of Photovoltaic Installation

Abstract Changes in the sun’s declination angle, every month for one year affect the energy received by a photovoltaic (PV). Changing the tilt angle, every month to follow changes in sun declination needs to be done so that the energy produced by PV is more optimum. Genetic algorithm (GA) was proposed in this study as the optimal PV slope solution every month of the year. GA calculates the slope angle of PV by calculating changes in sun declination, the movement of the sun from east to west, the coordinates of the location of PV installation, direct radiation and diffuse radiation. GA initializes tilt angle PV with the specified limit, then calculates to get the optimum total energy value on the inclined plane that PV can accept with the crossover, mutation and selection process. Algorithms are limited to 50 generations. The results showed that the optimization of the monthly period slope was better than the annual period. Installation with a slope angle after the monthly period optimization has increased the energy received by solar panels by 12 kWh/m 2 -year compared to installations with the slope of the annual period of optimization.
Empirical Evaluation of User Modeling Systems

This tutorial will introduce User Modeling (UM) researchers to the techniques of empirical evaluation of user modeling systems. No background in statistics is required. The target audience is UM researchers, especially students, who have a background in computer science or some other field that does not normally include designing and running human-subject experiments. Topics include designing experiments (choosing independent/dependent variables, covariant and nuisance variables, between vs. within subjects designs, factorial designs, estimating sensitivity, layered evaluation), running experiments (recruiting participants, controlling the environment, recording data), data analysis (statistical tests, ANOVA, checking assumptions of statistical methods, multiple testing correction, explained variance), and common surveys/tests for gathering covariate data.
Nuclei Classification in Histopathological Images via Local Phase Quantization and Convolutional Neural Network

Classification of nuclei patterns in histopathological images has a significant interest in a wide range of biomedical research. In recent years, deep learning approaches have solved different issues and shown promising results in histopathological images. These developments have assisted researchers to distinguish different types of pathological nuclei samples without high labeling cost. In the normal routine, examination of histopathological slides under a microscope might be sensitive, slow and inaccurate due to subjective evaluations. In this study, we aim two different types of machine learning approaches for automatic nuclei classification in histopathological images on University of Warwick's colon data set with four nuclei types: Epithelial, inflammatory, fibroblast and other. The first approach was based on a convolutional neural network-based feature extraction with support vector machine-based classification. In the second approach, local phase quantization (LPQ) is applied along with support vector machine-based classification with data set augmentation. The obtained results achieved high accuracy rates on the classification of nuclei in histopathological images.
Mendeteksi Kondisi Organ Liver Melalui Citra Iris Menggunakan Teknik Pengolahan Citra Digital

Besides Physical test normally, early detection on the condition of the body by using the image processing of iris is an alternative method to observe the health of human’s body, especially the internal organ of the body. This paper uses Dr. Bernard Jensen’s chart of iris as reference, in which part and how deep is the damage happens in the tissue of iris. Organ disorder is represented by the form of broken tissue of iris. The broken tissue usually seems to be like a hole in certain area in the iris. In this paper, the instrumentation for data mining uses video camera and the software that will be developed uses Visual Basic on image processing programming. In the image of eye, the region of interest is only on the iris, and it will be grabbed by using circle and line equations.The area of Liver organ lies on 07.15 – 07.45 in the third Quadran. After wards, this slice of image is prepared for image processing system. The method that is going to be used in this paper is grey level, enhancing and sobel operator. Then, the output of the system will be compared with physical test to measure the precision on detecting the problem on Liver organ.
Word Topic Prediction Model for Polysemous Words and Unknown Words Using a Topic Model

A prediction method for word topics is proposed. Sentences are sometimes difficult to read if the meaning of one or more words is unknown. However, they become easily readable if the topics of the words are known. Therefore, a topic model in which the words are divided by topic and several words are chosen from the available topics was used to predict the topics. However, the model performed poorly when dividing polysemous and unknown words by topic. To address this problem, a new method was formulated by adding two new elements to the conventional method. One of the elements comprised separating polysemous words according to their examples. The other element involved associating unknown words with sentences composed of known words. Several experiments were conducted using the proposed method, which achieved accuracies of $$\sim $$ 60% and $$\sim $$ 80% for dividing polysemous and unknown words, respectively, by topic. Such results imply the superiority of the proposed method for these two tasks.
Improving Quantum Metrology via Purifying and Distilling Noisy Probe States

Abstract In quantum metrology, the precision of unknown parameter estimation is studied in the quantum regime, and the choice of the probe state plays an important role in determining the precision of the parameter to be estimated. The quality of quantum metrology will be reduced in the presence of quantum noise during the memory time of probe states after preparation. Meanwhile the noisy probe state can be manipulated by different protocols such as single‐qubit purification, entanglement purification, and entanglement distillation etc. In this paper, the effects of these manipulations on the usefulness, that is, quantum Fisher information (QFI), of the noisy probe state in quantum metrology are studied. The results show that joint operations in single‐qubit purification and entanglement purification processes play positive roles in enhancing the QFI of the probe states, and local measurements in entanglement purification and entanglement distillation processes play both positive and negative roles in enhancing the QFI of the probe states. In this sense, single‐qubit purification will always be helpful in parameter estimation by using single qubits as probe, and entanglement purification process maybe more suitable for improving the estimation precision when entangled‐state probe is adopted.
Simulation of Performance Comparison between Discrete Wavelet Transform and Discrete Cosine Transform Methods for Radar Signal Processing in High-Noise Area

Abstract Detection of low signal and determination target locations are tremendously important in the radar system. Performance of radar can be enhanced by improving the signal-to-noise ratio of the received signal at the receiver. In this paper, we demonstrate an algorithm in radar signal processing, due to extract the signal target in the place of noise. This algorithm is based on Discrete Cosines Transform (DCT) and Discrete Wavelet Transform (DWT) methods. DWT signal processing analyses and comparatives with mother wavelet Haar, Daubechies-12, Coiflet-5 and Symlet-8. In addition, DCT signal processing analyses and comparatives with the same window function and uses to restrict the signal. Window function has influenced the signal resolution in frequency domain. Window functions that are used in this research are Rectangular, Hamming, Hanning and Dolph-Chebyshev. The results of the simulation and analysis show that mother wavelet with DWT, wavelet Daubechies-12 and Symlet-8 gives the best performance while mother wavelet Haar gives a bad performance. Wavelet Daubechies-12 gives the biggest signal to noise ratio that is 32.0603 dB. Mother wavelet Symlet-8 gives 32.6589 dB. Mother wavelet Haar gives 14.6692 dB. Testing DCT window function, window Dolph-Chebyshev gives the best performance, which gives the best separation of the signal. Analysis of signal reflection that accepted by radar shows the result that DWT is giving better performance than DCT in breaking of noise.
Linear Programming with Fuzzy Variable Method for Solving Wastewater Treatment Plant (WWTP) Problem

Abstract The waste load of organic matters in the stabilization ponds is degraded through biological treatment, that is to reduce or decrease the level of pollutants of organic matters by using and utilizing microorganisms. This problem will be solved using a mathematical model, that is linear programming with fuzzy variables on the Wastewater Treatment Plant (WWTP). The method in the case of Fuzzy Linear Programming (FLP) maximization is done by using simplex fuzzy method. The results of this method can be used as an evaluation method to determine the optimal waste load and the level of degradation of organic matter.
UMAP PATCH 2019 Chairs' Welcome

It is our great pleasure to welcome you to the ACM 2019 PATCH. Following the successful series of PATCH workshops, started in 2007, PATCH 2019 is organized as the meeting point between state of the art cultural heritage research and personalization - using any kind of technology, while focusing on ubiquitous and adaptive scenarios, to enhance the personal experience in cultural heritage sites. The workshop is aimed at bringing together researchers and practitioners who are working on various aspects of cultural heritage and are interested in exploring the potential of state of the art of personalized approaches that may enhance the CH visit experience.
A Machine for Living In

A Machine for Living In is a multi-layered digital media artwork using newly available computational and sensing tools to study the home as a site of intimate life. The project has two distinct phases: the construction and inhabitation of a functional smart home system, followed by an exhibition of processed data as a multi-part digital art installation. For ACM Designing Interactive Systems / Creativity and Cognition 2019, A Machine for Living In is installed as a multi-element piece in the gallery space consisting of a sensor narrative video, overhead camera video, and digital audio. All data were originally recorded in the artist's home over a period of weeks, and here are filtered through diverse processes of machine perception. In a process of joint human-machine authorship, this system produces a complex portrait of the home: as a space of language, intimacy, bodily practice, and quotidian narrative.
A Startup Assessment Approach Based on Multi-Agent and Blockchain Technologies

The dynamic nature of startups is linked to both high risks in investments as well as potentially important financial benefits. A key aspect to manage interactions among investors, experts, and startups, is the establishment of trust guarantees. This paper presents the formalization and implementation of a system enforcing trust in the startup assessment domain. To do so, an existing architecture has been extended, incorporating a multi-agent community and related interactions via private blockchain technology. The developed system enables a trust-based community, immutably storing, tracking, and monitoring the agents' interactions and reputations.
Detecting and Preventing File Alterations in the Cloud Using a Distributed Collaborative Approach

Cloud Computing is the new trend and this brings new issues and challenges in cyber security since a lot of the data from companies and users is available through the Internet. It is not only about the data but also about the applications that run in the Cloud that could be compromised affecting the service to thousands or millions of users, which may also have their local systems under siege through the exploitation of the security flaws in the Cloud. We propose here an algorithm that can detect unauthorized modifications to any of the files that are kept under custody. It is a collaborative systems where a set of nodes participate to gain also reputation points. This is a light algorithm that requires a time complexity of only O(n) times hashings in total. In our algorithm is implemented a technique to avoid the Hash Value Manipulation Attack that is one kind of Man-in-the-middle attack used to replace hash values. Any unauthorized modification of a file is detected and reported without the need of a third party auditor, which is another advantage.
labjack-controller: Robust and Easy Data Collection with Labjack T-Series DAQs in Python

The ability to accurately collect and computationally process data from sensors in both a logging and interactive sense is crucial to many scientific experiments.Researchers in many field are results driven and want to be able to easily and inexpensively gather data at high speeds with uniform sampling rates without having to invest weeks of time learning a complicated API.Common (expensive) paid solutions are found in Digital Acquisition Devices (DAQs) provided by companies like National Instruments (NI).These pre-built DAQs are generally understood to be tied to proprietary analysis software or languages, such as NI's paid language LabView.In light of this, there has been a recent surge in DAQ offerings from multiple manufacturers to capture the market.These solutions tend to have cost-effective, versatile hardware, but interfaces that are poorly designed or immature ("Model 301/302 developer application software," 2017), which makes it difficult for non-specialists to use.Current offerings from Labjack ("Labjack," 2019) are one such example; their hardware is used in major industrial applications, but the interface is exceedingly nuanced and provides little in the way of error recovery.
Gesture Recognition Based on Multi-Channel Convolutional Neural Network

Gesture recognition is widely used in life, but single-perspective gesture recognition has certain limitations. In this paper, we use the multi-channel convolutional network of this paper to train and use the Multi-perspective Static Gesture Database constructed in this paper for experimental training. The database has 24 letter gestures in the international sign language alphabet (except for j gestures and z gestures). Using several independent channels with the same convolution kernel to extract the gesture pictures of each perspective, the features of each perspective can be extracted, then feature fusion is performed in the fully connected layer, and finally classified by the Softmax classifier. By contrast, the prediction accuracy of the network four-perspective combination is higher than that of a single picture. At the same time, further research is carried out to compare the three-perspective combination and the two perspective combinations respectively, and the final experimental results are obtained. The reasonable selection of the angle of perspective of the gesture shooting can improve the accuracy of gesture recognition.
Naturalness Preserved Image Aesthetic Enhancement with Perceptual Encoder Constraint

Typical supervised image enhancement pipeline is to minimize the distance between the enhanced image and the reference one. Pixel-wise and perceptual-wise loss functions could help to improve the general image quality, however are not very efficient in improving the image aesthetic quality. In this paper, we propose a novel Residual connected Dilated U-Net (RDU-Net) for improving the image aesthetic quality. By using different dilation rates, the RDU-Net can extract multiple receptive-field features and merge the maximum information from local to global, which are highly desired in image enhancement. Also, we propose an encoder constraint perceptual loss, which could teach the enhancement network to dig out the latent aesthetic factors and make the enhanced image more natural and aesthetically appealing. The proposed approach can alleviate the over-enhancement phenomenons. The experimental results show that the proposed perceptual loss function could give a steady back propagation and the proposed method outperforms the state-of-the-arts.
Development of Teaching Materials for Routing of Network Using a Full Color LED Tape as Physical Interface

In Japan, the new course of study for technology education of junior high schools and for Information education of high schools included the contents of network. However, there are little teaching materials for these studies. Thus it is difficult students image network systems and technologies, because students have few realities for network systems. So we have been developing teaching materials for experimental studying of routing technologies based on concept of physical visualizations.
Using SMART Table Technology for the Interactive Elementary Classroom

The development of new technologies enables learners to learn more effectively, efficiently, flexibly, and comfortably. The utilization of the SMART Table, a multi-touch table that can capture and identify numerous objects simultaneously when placed on its surface, can provide a more interactive and participative environment in classrooms. This chapter examined the perceptions of using SMART Table in the elementary classroom. The expectations were that SMART Table applications will help to enhance, engage, and support elementary students in their learning. Data was collected through means of observation, student work samples, survey, and personal interview. The findings suggest that there are many benefits for educators to incorporate the SMART Table into daily instruction at elementary classrooms. Increased student engagement, increased peer collaboration, and strengthening comprehension are some of those key benefits. The study implications determined that the SMART Table can provide elementary students with an engaging outlet of instruction.