Reviewerly/exHarmony huggingface dataset:
IDENTIFIKASI MOTIF BATIK TULIS WONOSOBO DENGAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM)

Various Batik becomes one of the characteristics of the Indonesian region is no exception of Wonosobo. Many types and similarities of batik make it difficult to recognize the type of batik for sure. This research aims to computerize the process of identification of the Wonosobo batik motif by applying the method of the Gray Level Co-occurrence Matrix (GLCM). This method is one of the statistical methods that can be used to analyze feature extraction. After the characteristic extraction of each batik motif is known, the next stage is classification. Batik motifs will be classified according to the test image into a predefined group or class, at the stage of this classification method used is the method K-Nearest Neighbour (K-NN). The test data is used in the form of a Wonosobo batik image of 256x256 pixels formatted as. jpg as much as 15 data. From each type of highest accuracy batik is found in Carica batik is 85.71% and the accuracy of horse braid batik is 66.67% and 83.3% against batik Purwaceng. Hence the overall accuracy of the system is 83.3%.
Pengembangan Media Dart Board Untuk Meningkatkan Hasil Belajar Ipa Kelas Ii Di Sdit Al Akbar Karanggayam Srengat Blitar

The aim of this study was to explore how the process of developing and testing the feasibility of the Dart Board as a learning medium to improve student learning outcomes in class II science subjects at SDIT Al Akbar Karanggayam Srengat Blitar. The research method used is research and development (R & D), which is a research method used to produce certain products and to test the effectiveness of these products. The results showed that the effect of using dart board media on student learning outcomes in class II-B showed the percentage of completeness by looking at the comparison before and after using dart board media, namely before treatment with dart board media was 8.33% so that there were 91.6% students who had not complete. However, after receiving treatment using dart board media, it increased to 87.5% and of course 12.5% of students were categorized as incomplete.
Educational research as a threshold of meanings to come

In this dossier, we propose to install the question about criticism, about how to remain critical in times of digitization of culture that seems to drag with it precisely any perspective of knowing that is not willing to think beyond the drifts of applicability. In this sense, educational research opens as a journey, as a search for readings, techniques, developments and forms of conceptualization that bring us closer to understanding what schooling is being today. The group of articles that make up this special dossier on educational research opens up new meanings, as thresholds that project us to take the floor in public affairs and delves into the threads, the power relations and the crystallizations of such a social practice. Central as is the one that allows those who come to the world to be part of it.
Robustness via Probabilistic Cross-Task Ensembles

We present a method for making predictions using neural networks that, at the test time, is robust against shifts from the training data distribution. The proposed method is based on making \emph{one prediction via different cues} (called middle domains) and ensembling their outputs into one strong prediction. The premise of the idea is that predictions via different cues respond differently to distribution shifts, hence one can merge them into one robust final prediction, if ensembling can be done successfully. We perform the ensembling in a straightforward but principled probabilistic manner. The evaluations are performed using multiple vision dataset under a range of natural and synthetic distribution shifts which demonstrate the proposed method is considerably more robust compared to its standard learning counterpart, conventional ensembles, and several other baselines.
Developments in Intellectual Property Law: October 1, 2017-September 30, 2018

BackgroundThe America Invents Act of 2011 created the inter partes review process as a means for reexamining the validity of previously-issued patents. 9An inter
Adaptive Multi-prototype Relation Network

Based on Relation Network, we propose a new network structure that can adaptively adjust the number of prototypes according to data distribution. Our method, called the Adaptive Multi-prototype Relation Network(AMRN), aims at extracting more reasonable prototype representation for different data distribution in few-shot learning case. Instead of representing each class as a single prototype in the relational network, we represent each class with one or more prototypes, and solve the problem of embedding network with the relational network connection, which can improving the classification accuracy in few-shot learning. Besides, our method can easily extend to other network structures, which is also a useful reference for other metric learning approaches.
The influence of explanations in recommender systems on user engagement

Recommender systems are without a doubt a staple of the modern internet. Services like Amazon, Netflix, YouTube and Spotify rely on them. What makes them so engaging that millions of users spent bi ...
Just noticeable difference for machine perception and generation of regularized adversarial images with minimal perturbation

In this study, we introduce a measure for machine perception, inspired by the concept of Just Noticeable Difference (JND) of human perception. Based on this measure, we suggest an adversarial image generation algorithm, which iteratively distorts an image by an additive noise until the model detects the change in the image by outputting a false label. The noise added to the original image is defined as the gradient of the cost function of the model. A novel cost function is defined to explicitly minimize the amount of perturbation applied to the input image while enforcing the perceptual similarity between the adversarial and input images. For this purpose, the cost function is regularized by the well-known total variation and bounded range terms to meet the natural appearance of the adversarial image. We evaluate the adversarial images generated by our algorithm both qualitatively and quantitatively on CIFAR10, ImageNet, and MS COCO datasets. Our experiments on image classification and object detection tasks show that adversarial images generated by our JND method are both more successful in deceiving the recognition/detection models and less perturbed compared to the images generated by the state-of-the-art methods, namely, FGV, FSGM, and DeepFool methods.
LibGuides: Natural Sciences Digital Library: Tech Instructional Coach Tips

Ebooks, magazines, videos, and other online content especially for biology, chemistry, physics, geology, environmental systems, earth space science, and health and medicine.
Privacy-Preserving Teacher-Student Deep Reinforcement Learning.

Deep reinforcement learning agents may learn complex tasks more efficiently when they coordinate with one another. We consider a teacher-student coordination scheme wherein an agent may ask another agent for demonstrations. Despite the benefits of sharing demonstrations, however, potential adversaries may obtain sensitive information belonging to the teacher by observing the demonstrations. In particular, deep reinforcement learning algorithms are known to be vulnerable to membership attacks, which make accurate inferences about the membership of the entries of training datasets. Therefore, there is a need to safeguard the teacher against such privacy threats. We fix the teacher's policy as the context of the demonstrations, which allows for different internal models across the student and the teacher, and contrasts the existing methods. We make the following two contributions. (i) We develop a differentially private mechanism that protects the privacy of the teacher's training dataset. (ii) We propose a proximal policy-optimization objective that enables the student to benefit from the demonstrations despite the perturbations of the privacy mechanism. We empirically show that the algorithm improves the student's learning upon convergence rate and utility. Specifically, compared with an agent who learns the same task on its own, we observe that the student's policy converges faster, and the converging policy accumulates higher rewards more robustly.
Membership Attacks on Conditional Generative Models Using Image Difficulty

Membership inference attacks (MIA) try to detect if data samples were used to train a Neural Network model. As training data is very valuable in machine learning, MIA can be used to detect the use of unauthorized data. Unlike the traditional MIA approaches, addressing classification models, we address conditional image generation models (e.g. image translation). Due to overfitting, reconstruction errors are typically lower for images used in training. A simple but effective approach for membership attacks can therefore use the reconstruction error. However, we observe that some images are universally easy, and others are difficult. Reconstruction error alone is less effective at discriminating between difficult images used in training and easy images that were never seen before. To overcome this, we propose to use a novel difficulty score that can be computed for each image, and its computation does not require a training set. Our membership error, obtained by subtracting the difficulty score from the reconstruction error, is shown to achieve high MIA accuracy on an extensive number of benchmarks.
Dual attention and part drop network for person reidentification

Pedestrian occlusion, variations in the cross-view angle, and the appearances of pedestrians significantly hinder person reidentification (ReID). A dual attention and part drop network (DAPD-Net) for person ReID is proposed. The dual attention module enables the deep neural network to focus on the pedestrian in the foreground of a given image and weakens background perturbance. It can speed up learning and improve network performance. Feature maps in the part drop branch that we proposed are divided into multiple parts, one of them is randomly dropped, and the remainder are learned to obtain a feature that is robust against occlusion. Through part drop training, the antiocclusion ability of the network is effectively improved. The middle-layer branch is used, which help our network to learn mid-level semantic feature and promote capability of the system. These innovative modules can help deep neural network to extract discriminative feature representations. We conduct extensive experiments on multiple public datasets of person ReID. The results show that our method outperforms many state-of-the-art methods.
Language-agnostic Incremental Code Clone Detection

Code duplication is a form of technical debt frequently observed in software systems. Its existence negatively affects the maintainability of a system in numerous ways. In order to tackle the issues that come with it, various automated clone detection techniques have been proposed throughout the years. However, the vast majority of them operate using the entire codebase as input, resulting in redundant calculations and undesirable delays when this process is repeated for every new revision of a project. On the other hand, newer incremental techniques address this by storing intermediate information that can be reused across revisions. However, all these approaches are language-specific, utilizing language parsers to generate more sophisticated source code representations, in an attempt to detect more complex types of clones. As a result, less popular languages, for which finding or building a parser is challenging, are unfortunately not supported. In this study we propose LIICD, a language-agnostic incremental clone detector, capable of detecting exact-match clones. We assess its performance and compare it with a state-of-the-art commercial-grade detector, found within the Software Improvement Group (SIG). Furthermore, we use a similarity estimation technique called Locality Sensitive Hashing (LSH) in an attempt to extend and improve the original approach. Our experiments result in some interesting findings. Firstly, the proposed incremental detector is very efficient and able to scale well for larger codebases. Additionally, it provides a significant improvement compared to a non-incremental commercial-grade detector. Lastly, our LSH-based extension proves to have difficulties matching our original approach's performance. However, future suggestions indicate how the potential of the technique can be further investigated.
Picking Automatization by Pattern Matching: A New Methodology Using EAT Events

Summary The drastic increase of the observed seismicity at fluid injection sites has led to the need for further development of automatic picking methods for seismic events. Here, we focused on a pattern matching technique, which has traditionally relied on a representative Master event. However, this method strongly depends on the selection of the Master Event, which can be challenging with low Signal to Noise Ratios (SNR) or data gaps. In this study, we developed the Empirically Aggregated Template (EAT) concept. An EAT is a type of Master Event built using the seismic traces with the best SNR at each sensor, based on a set of events that exhibit high waveform similarity, and which are spatially close to one another. Thus, an EAT event will present the highest possible SNR of the database, and is not affected by data gaps. The efficiency and quality of this methodology has been tested by comparing with a set of manually picked events. The picking accuracy, location error, and travel-time residuals demonstrate similarities between manually and EAT picked events and allow us to validate this method. Finally, we have highlighted the advantages and improved results obtained using an EAT compared to a standard Master Event.
Optimasi Manajemen Rapat di Masa Pandemi Covid-19Berbasis Aplikasi SeMaR

ABSTRACTThe meeting activity is one of the routine and scheduled activities that is definitely carried out in every institution, as well as the ITN Malang campus. Problems arise when Covid-19 begins to plague in the city of Malang so that teaching and learning activities on campus must be closed and demanded to work from home according to government recommendations. Difficulties that arise are the way to send invitations and record the results of meetings that have not been efficient. So, it is necessary to develop an application that functions to manage meetings in the ITN Malang internal environment named SeMaR (Meeting Management System). This application was developed based on web and Android using design sprint development method. The results of the analysis and testing show that the design of applications that involve potential users can produce an application that is truly in accordance with user requirements. In addition, the use of the SeMaR application to manage meeting activities in the ITN Malang is able to optimize employee performance especially during the Covid-19 pandemic, and meeting management can be more effective and efficient.Keywords: meeting management, efficiency, application, android, website
An Improved Belief Rule-Based Expert System with an Enhanced Learning Mechanism

Belief rule-based expert systems (BRBESs) are widely used in various domains which provide an integrated framework to handle qualitative and quantitative data by addressing several kinds of uncerta ...
Mechanisms of Online Control of Speech

Author(s): Raharjo, Inez | Advisor(s): Nagarajan, Srikantan S | Abstract: Speech is central to human communication. One of the features of speech we control to make distinguishable speech is formants, which are the spectral frequencies in our speech that we vary to make different vowel sounds. It is well-understood that the control of speech features, including formants, depends heavily on feedback information. However, the exact mechanisms of feedback control of formants are not fully understood. The body of this dissertation investigates the feedback control of formants in different scenarios to explore its relationship with other speech control mechanisms. We first investigated the relationship of feedback control and the adaptation of feedforward control of formants and found that they have distinct mechanisms. We then explored the neural substrate of feedback processing for formants as compared to pitch using MEG, which allowed us to investigate network activity changes with a high temporal resolution. We found similar neural regions involved in feedback processing for both pitch and formants, though we observed opposite lateralization for pitch and formants across different frequency bands. Finally, we explored the interaction between auditory and somatosensory feedback information in the control of formants by attenuating oral somatosensory feedback using lidocaine while perturbing auditory feedback. We found a direction-dependent interaction between the two feedback modalities, specifically where we observed an increase in compensation responses to downward auditory feedback perturbations but a decrease in compensation responses to upward auditory feedback perturbations when the oral somatosensory feedback was attenuated.
Achieving Explainability in a Visual Hard Attention Model through Content Prediction

A visual hard attention model actively selects and observes a sequence of subregions in an image to make a prediction. Unlike in the deep convolution network, in hard attention it is explainable which regions of the image contributed to the prediction. However, the attention policy used by the model to select these regions is not explainable. The majority of hard attention models determine the attention-worthy regions by first analyzing a complete image. However, it may be the case that the entire image is not available in the beginning but instead sensed gradually through a series of partial observations. In this paper, we design an efficient hard attention model for classifying partially observable scenes. The attention policy used by our model is explainable and non-parametric. The model estimates expected information gain (EIG) obtained from attending various regions by predicting their content ahead of time. It compares EIG using Bayesian Optimal Experiment Design and attends to the region with maximum EIG. We train our model with a differentiable objective, optimized using gradient descent, and test it on several datasets. The performance of our model is comparable to or better than the baseline models.
PENGGUNAAN APLIKASI PERPUSTAKAAN DIGITAL “PERPUSDA WONOSOBO” DAN SOSIALISASI PERPUSTAKAAN DI RADIO SELAMA PANDEMI

Pandemic conditions, which require people to limit their space, have increased their need for information. This pandemic has also had a major impact on libraries. The most pronounced impact is the service that is not optimal because there are rarely visitors and savings so that it only serves borrowing and book services. This pandemic also makes regional libraries a digital library application and provides education about libraries via radio.  This study aims to examine the library during the pandemic, how the benefits of using the digital library application “Perpusda Wonosobo”, and the analysis of having radio mass media in providing educational information about the library. This research uses the descriptive qualitative method. How to obtain data is structured interviews and observations. Researchers see what, where, and how the subject will be extracted information. The results of this study indicate that marketing through promotion makes the use of the digital library application “Perpusda Wonosobo” statistically increased during the pandemic. More and more people are using this digital library application to meet their information needs without having  to  interfere  with  the  mobility  of  life  during a pandemic. The use of radio media is also an effective secret to introduce to the general public about libraries and education from the library itself. Strong radio makes educational broadcasts more attractive and can reach the public. Furthermore, from this study, more and more people are using digital library applications because they are available in access and use. In the use of radio, information about the library can reach rural areas so that listeners from various backgrounds can listen to it because it is delivered in a language that is easily recognized. Introduction to libraries can also reach areas that have difficulty accessing the internet.
Catalytic quantum teleportation

Quantum catalysis is an intricate feature of quantum entanglement. It demonstrates that in certain situations the very presence of entanglement can improve one's abilities of manipulating other entangled states. At the same time, however, it is not clear if using entanglement catalytically can provide additional power for any of the existing quantum protocols. Here we show, for the first time, that catalysis of entanglement can provide a genuine advantage in the task of quantum teleportation. More specifically, we show that extending the standard teleportation protocol by giving Alice and Bob the ability to use entanglement catalytically, allows them to achieve fidelity of teleportation at least as large as the regularisation of the standard teleportation quantifier, the so-called average fidelity of teleportation. Consequently, we show that this regularised quantifier surpasses the standard benchmark for a variety of quantum states, therefore demonstrating that there are quantum states whose ability to teleport can be further improved when assisted with entanglement in a catalytic way. This hints that entanglement catalysis can be a promising new avenue for exploring novel advantages in the quantum domain.
InvertGAN: Reducing mode collapse with multi-dimensional Gaussian Inversion

Generative adversarial networks have shown their ability in capturing high-dimensional complex distributions and generating realistic data samples e.g. images. However, existing models still have difficulty in handling multi-modal outputs, which are often susceptible to mode collapse in the sense that the generator can only map latent variables to a part of modes of the target distribution. In this paper, we analyze the typical cases of mode collapse and define the concept of mode completeness in the view of probability measure. We further prove that the inverse mapping can play an effective role to mitigate mode collapse. Under this framework, we further adopt the multi-dimensional Gaussian loss instead of one-dimensional one that has been widely used in existing work, to generate diverse images. Our experiments on synthetic data as well as real-word images show the superiority of our model. Source code will be released with the final paper.