Reviewerly/exHarmony huggingface dataset:
Frank-Wolfe for Sign-Constrained Support Vector Machines

Domain knowledge is useful to improve the generalization performance of learning machines. Sign constraints are a handy representation to combine domain knowledge with learning machine. In this paper, we consider constraining the signs of the weight coefficients in learning the linear support vector machine, and develop two optimization algorithms for minimizing the empirical risk under the sign constraints. One of the two algorithms is based on the projected gradient method, in which each iteration of the projected gradient method takes $O(nd)$ computational cost and the sublinear convergence of the objective error is guaranteed. The second algorithm is based on the Frank-Wolfe method that also converges sublinearly and possesses a clear termination criterion. We show that each iteration of the Frank-Wolfe also requires $O(nd)$ cost. Furthermore, we derive the explicit expression for the minimal iteration number to ensure an $\epsilon$-accurate solution by analyzing the curvature of the objective function. Finally, we empirically demonstrate that the sign constraints are a promising technique when similarities to the training examples compose the feature vector.
An Efficient Multimodal Aggregation Network for Video-Text Retrieval

In video-text retrieval task, mainstream framework consists of three parts: video encoder, text encoder and similarity calculation. MMT (Multi-modal Transformer) achieves remarkable performance for this task, however, it faces the problem of insufficient training dataset. In this paper, an efficient multimodal aggregation network for video-text retrieval is proposed. Different from the prior work using MMT to fuse video features, the NetVLAD is introduced in the proposed network. It has fewer parameters and is feasible for training with small datasets. In addition, since the function of CLIP (Contrastive Language-Image Pre-training) can be considered as learning language models from visual supervision, it is introduced as text encoder in the proposed network to avoid overfitting. Meanwhile, in order to make full use of the pre-training model, a two-step training scheme is designed. Experiments show that the proposed model achieves competitive results compared with the latest work.
Graph Embedding with Outlier-Robust Ratio Estimation

The purpose of graph embedding is to learn a lower-dimensional embedding function for graph data. Existing methods usually rely on maximum likelihood estimation (MLE), and often learn an embedding function through conditional mean estimation (CME). However, MLE is well-known to be vulnerable to the contamination of outliers. Furthermore, CME might restrict the applicability of the graph embedding methods to a limited range of graph data. To cope with these problems, this paper proposes a novel method for graph embedding called the robust ratio graph embedding (RRGE). RRGE is based on the ratio estimation between the conditional and marginal probability distributions of link weights given data vectors, and would be applicable to a wider-range of graph data than CME-based methods. Moreover, to achieve outlier-robust estimation, the ratio is estimated with the γ-cross entropy, which is a robust alternative to the standard cross entropy. Numerical experiments on artificial data show that RRGE is robust against outliers and performs well even when CME-based methods do not work at all. Finally, the performance of the proposed method is demonstrated on realworld datasets using neural networks.
Analisis Kejenuhan Mahasiswa dalam Mengikuti Perkuliahan Daring

This study aims to analyze and understand the factor of student boredom in attending online lectures during the Covid-19 pandemic and how appropriate learning strategies are in online lectures. The research method uses a qualitative approach to phenomenology. Sources of data were obtained from two sources, namely primary data sources generated from interviews with students and lecturers and secondary data as reinforcement of the results of interviews and observations that had been carried out. Data collection system is done through interviews, observation and documentation. The data analysis technique uses the Miles and Huberman analysis model which consists of three processes, namely data reduction, data presentation, and drawing conclusions. The results of the study indicate that various problems arise in the implementation of online lectures, ranging from the readiness of lecturers and students, the technological infrastructure they have and the internet network. Therefore, in overcoming the various problems that exist, researchers have found strategies that can be applied to minimize the level of student boredom in attending online lectures, namely by developing online learning methods that are more flexible and can expand the use of learning media by utilizing networks. social.
Rancang bangun mesin sablon cup semi otomatis

Screen printing is a technique of printing graphic designs using gauze or called screens. One application of the screen printing machine is on the cup. The more beverage vendors in cup packaging like today, the more screen printing machines are needed. To meet these needs, a cup screen printing machine was made. The purpose of the study was to determine the shape and dimensions as well as the performance of the semi-automatic cup screen printing machine. The research method carried out is by designing and manufacturing a semi-automatic cup screen printing machine and testing with two different types of molding, namely 14 Oz and 16 Oz. From the results of the design and manufacture of the semi-automatic cup screen printing machine design, the length is 600 mm, the width is 400 mm and the height is 900 mm. Using an electric motor of 0.25 HP, gear box 1:60, screen length of 400 mm and width of 150 mm and using a steel frame ST 37 type L. Based on the results of tests and calculations it is known that the production capacity of the screen printing machine is 300 cups/hour and the quality production with 14 Oz molding type is 92% good, 4.67% normal, 2.6% bad while 16 Oz molding is 88% good, 7.34% normal, 4.6% bad. The electrical power consumption of the screen printing machine is 328.6 Watt and the mechanical efficiency is 54.04 %.
MAPPING THE UTILIZATION OF AUGMENTED REALITY MEDIA IN VOCATIONAL EDUCATION IN DIY USING K-MEANS

The purpose of this study is to reveal group data on the use of augmented reality media in teaching and learning activities so that it is easier to improve student learning outcomes. Augmented reality media was chosen based on the complexity of the scope of teaching materials consisting of thinking skills and limited practice during the implementation of distance learning. The approach for grouping the use of augmented reality media is through the K-Means algorithm with R software. The sample of this study was 136 students of Building Information and Modeling Design skills competency from 5 State Vocational High Schools spread across districts and cities, DIY Province. The review aspect used as the basis for grouping is the need for alternative media in augmented reality, ease of use, supporting facilities, and speed of understanding the material. The grouping results explain that as many as 80 students can master the material well through augmented reality media, and it is appropriate to use it during distance learning. In comparison, the remaining 56 students still find it difficult. Through the results of this grouping, it can be used to combine other media for students who still find it difficult when participating in distance learning
Blind Image Inpainting with Mixture Noise Using <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>ℓ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </math> and Total Regularization

The blind image inpainting problem need to be handle when faced with a large number of images, especially medical images in medical health. For the proposed nonconvex sparse optimization model, a proximal based alternating direction method of multipliers (PADMM) method is designed to solve the problem. Firstly, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msub> <mrow> <mi>ℓ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </math> sparse regularization is imposed to the binary mask since the missing pixels are sparse in our experiments. Secondly, the total variation term is utilized to describe the underlying clean image. Finally, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msub> <mrow> <mi>ℓ</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </math> regularization of the fidelity term is used to solve the given blind inpainting problem. Experiments show that this method has better performance than traditional method, and could deal with the blind image inpainting problem.
COPRAO: A Rule-Based Capability-Aware Query Optimizer for an FPGA-Based Reconfigurable Near-Data Processor

Abstract Placing the processing power near the data, rather than shipping the data to the processor is inevitable and demanding in the era of Big Data. With a given near-data processor, it is important to use it properly to reduce the massive data transfer between data sources and computing nodes. In database query processing, as many operations as possible should be sent to the near-data processor, because it can process them at line-rate and thus can eliminate massive data transfer across the network. Unfortunately, there is still not enough support for effective utilization of hardware capabilities to solve the issues of data processing in query processors. In this paper, we propose a new approach to the query optimization in a relational DBMS, which considers the specialized computing capabilities of the attached FPGA-based near-data processor. We use extended rules to achieve hardware-conscious optimization and refine the optimization strategies according to changes in the hardware state. Our evaluations demonstrate that the proposed query-optimization approach can improvethe processing of queries.
Utilization of smartphones in making learning videos for teachers of ABA Kindergarten Sidoharjo

The government's policy of implementing the Work from home system as a form of handling the spread of the COVID-19 virus affects many aspects, including education. Education, which was previously carried out face-to-face, has been shifted to online learning. Online learning can be done using a number of formats, one of which is a video that contains elements of images and sound. Video creation can be produced simply, for example, using a smartphone, but not all teachers are able to optimize the tool. Therefore, empowerment team in collaboration with ABA kindergarten Sidoharjo, Sleman, Daerah Istimewa Yogyakarta was carried out with the aim of providing training to teachers in optimizing the use of smartphones as a means of producing learning video content. The activity begins with the pre-production stage, namely observation and discussion, the production stage, namely training in learning video production with smartphones, the post-production stage, namely editing training with the VN application on smartphones. Besides, empowerment team also provided grants green screen, audio equipment, and a set of studio lights for video production. The result of this program showed that teachers are able to improve the quality of learning videos just by using a smartphone.
Reasoning about manipulation in multi-agent systems

Selfish, dishonest or malicious agents may find an interest in manipulating others. While many works deal with designing robust systems or manipulative strategies, few works are interested in defining in a broad sense what is a manipulation and how we can reason with such a notion. In this article, based on a social science literature, we give a general definition of manipulation for multi-agent systems. A manipulation is a deliberate effect of an agent – called manipulator – to instrumentalize another agent – called victim – while making sure to conceal that effect. We present then a logical framework, called KBE, to express and reason about manipulations. Since manipulation relies on deliberate effects, KBE introduces a deliberate BIAT operator which abstracts deliberate consequences of actions. We prove that this logic is sound and complete. Furthermore,we express related notions such as coercion, persuasion, or deception.
Quantum median filter for total variation image denoising

In this new computing paradigm, named quantum computing, researchers from all over the world are taking their first steps in designing quantum circuits for image processing, through a difficult process of knowledge transfer. This effort is named Quantum Image Processing, an emerging research field pushed by powerful parallel computing capabilities of quantum computers. This work goes in this direction and proposes the challenging development of a powerful method of image denoising, such as the Total Variation (TV) model, in a quantum environment. The proposed Quantum TV is described and its sub-components are analysed. Despite the natural limitations of the current capabilities of quantum devices, the experimental results show a competitive denoising performance compared to the classical variational TV counterpart.
Modeling Individual Humans via a Secondary Task Transfer Learning Method

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Airplane Detection Based on Unsupervised Deep Domain Adaptation in Remote Sensing Images

Abstract In this work, we use the detection and localization capabilities of the pre-trained Faster Region Convolutional Neuronal Network (Faster R-CNN) model including Resnet50 as the backbone of the architecture. Our model has been trained with a huge benchmark dataset Common Objects in Context (MSCOCO) as a source domain. The model pre-trained is used in Unsupervised Deep Domain adaptation (UDDA) for airplane detection and localization in Remote Sensing Image (RSI) as a domain target. We evaluate our proposed approach using images containing multi-objects (airplanes) on a different scale and types collected from the public dataset for Object Detection in Aerial Images (DOTA) and different airport images extracted from Google Earth. Extensive experiments in a Cloud environment reveal the usefulness of the proposed approach regarding score. UDDA algorthm proposed is a non deterministic machine learning approach for detecting the airplane in RSI.
Empowerment of Alam Kalijaga reading house to improve literacy culture of the Nasri Hamlet, Grabag, Magelang

The lack of literacy culture in students such as reading literacy, scientific literacy, and digital literacy makes it difficult for parents to accompany students in distance learning activities at home. Therefore, this service activity was carried out with the aim of increasing the literacy culture in students through the use of the Alam Kalijaga Reading House located in Nasri Hamlet, Sidogede, Grabag, Magelang. The stages of implementing this program are planning, implementation, and evaluation. The activities given are inviting children to learn together to increase their interest in reading, which begins with socializing the importance of current literacy, practicing using a laptop, and labeling plant names and their benefits. In this way, it supports students and parents in the distance learning process and the creation of a literacy culture.
Developing digital learning materials using whiteboard animation for middle and high schools

Educators are encouraged to develop their technology integration abilities as information and communication technology (ICT) advances in the field of education. This has ramifications for school curriculum, which must be able to adapt to changing circumstances in order to satisfy the educational demands of pupils during the industrial revolution. 4.0. Training on the construction of digital learning materials utilizing whiteboard animation is one of the initiatives to overcome the aforementioned difficulties. After 50 hours of training, the teachers at 'Aisyiyah Middle and Hight Schools (SMP and SMA) in the Lawang sub-district were able to generate instructional materials by constructing material situations, which were then translated into Whiteboard animation. The teachers noted that understanding the chosen application was tough at first, but with effort and the help of the training materials, everyone was able to make their first whiteboard animation. The vast majority of participants want to use this program to create further materials. It is also hoped that in the future, similar materials or their development will be available.
Assisting the management of digital library in Tegal Harum Village Denpasar

This community service activity under the scheme of applying science and technology for the community is in partnership with the village of Tegal Harum, Denpasar Bali, in the field of digital library management. The Sastra Mahottama Library in Tegal Harum Village, apart from providing conventional services, also provides digital library services through the SIPESAT application (Digital Library Application) in Tegal Harum Village, which makes it easier for people to borrow various types of e-books according to their needs. Problems faced by the library managers are related to the classification and cataloguing of library materials, as well as increasing the capacity of human resources for digital library managers. The implementation using the classical and individual methods lasts for one month at the Tegal Harum village office. Activities consist of situation analysis, training, and evaluation stages. The implications of service activities are the results of the classification and cataloguing of 30% of reading collections as well as increasing knowledge and skills in using digital library applications.
Bootstrapping HDR video quality analysis from SDR via data-adaptive grading

High Dynamic Range (HDR) video in Ultra-High Definition (UHD) has been in use now for a number of years, in streaming services like Netflix and Amazon Prime and even YouTube, as well as in broadcast, on blu-ray, and can even be captured by consumers using high-end cameras and drones. These applications typically use HDR video with wide color gamut (BT.2020 color space) compressed in the H.265/HEVC video codec. While HDR video is thus by now ubiquitous, measuring the quality of HDR video objectively remains challenging. First, the peak-signal-to-noise ratio (PSNR), never a high-quality measure, becomes even less reliable in HDR. Moreover, with the wide color gamut typically used, one would expect color space distortion measures to play an essential role; several have been tested in the literature, but none has proven reliable. Finally, while there exists both visual quality databases for HDR images, and HDR image quality measures, there is virtually no widely available database for HDR video (we understand that there are some in development), nor a single, widely recognized HDR video quality measure. On the other hand, the field of video quality analysis is by now well-established for standard dynamic range (SDR) video, and FastVDO has developed such a measure (FastVDO Quality, FVQ). In this paper, we propose to make progress in HDR Video Quality Analysis (VQA) by initiating a novel bootstrapping method to measure the quality of HDR video by developing a dynamic conversion of HDR to standard dynamic range (SDR), and combining the video quality measure of the SDR using the FVQ measure previously developed, and a measure for the surplus quality of the HDR over the SDR video. The central conversion function is itself based on an approach previously developed by FastVDO and described in standard documents and presented at SPIE.
Image to Language Understanding: Captioning approach

Extracting context from visual representations is of utmost importance in the advancement of Computer Science. Representation of such a format in Natural Language has a huge variety of applications such as helping the visually impaired etc. Such an approach is a combination of Computer Vision and Natural Language techniques which is a hard problem to solve. This project aims to compare different approaches for solving the image captioning problem. In specific, the focus was on comparing two different types of models: Encoder-Decoder approach and a Multi-model approach. In the encoder-decoder approach, inject and merge architectures were compared against a multi-modal image captioning approach based primarily on object detection. These approaches have been compared on the basis on state of the art sentence comparison metrics such as BLEU, GLEU, Meteor, and Rouge on a subset of the Google Conceptual captions dataset which contains 100k images. On the basis of this comparison, we observed that the best model was the Inception injected encoder model. This best approach has been deployed as a web-based system. On uploading an image, such a system will output the best caption associated with the image.
A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space

In cross-lingual language models, representations for many different languages live in the same space. Here, we investigate the linguistic and non-linguistic factors affecting sentence-level alignment in cross-lingual pretrained language models for 101 languages and 5,050 language pairs. Using BERT-based LaBSE and BiLSTM-based LASER as our models, and the Bible as our corpus, we compute a task-based measure of cross-lingual alignment in the form of bitext retrieval performance, as well as four intrinsic measures of vector space alignment and isomorphism. We then examine a range of linguistic, quasi-linguistic, and training-related features as potential predictors of these alignment metrics. The results of our analyses show that word order agreement and agreement in morphological complexity are two of the strongest linguistic predictors of cross-linguality. We also note in-family training data as a stronger predictor than language-specific training data across the board. We verify some of our linguistic findings by looking at the effect of morphological segmentation on English-Inuktitut alignment, in addition to examining the effect of word order agreement on isomorphism for 66 zero-shot language pairs from a different corpus. We make the data and code for our experiments publicly available.
Inactive medical record management at Bandung Hospital

The purpose of this study was to examine the management of inactive medical records in a private hospital in Bandung. The research method used is descriptive-analytic. The research subjects were the head of the medical records section and the storage officer. The results of the study were in inactive medical record storage in the form of microfilm and USB as well as computer notebooks. The medical record room does not have room temperature control, humidity control room, and lighting settings. Maintain the cleanliness of the medical record room and inactive medical records. There is no treatment for inactive medical record files, air exchange, and medical record maintenance. Storage of inactive medical records uses a centralized and decentralized system. Requirements for inactive medical record storage rooms for temperature control, storage cabinets, and room humidity do not use regulators, and there is no supervision.