Reviewerly/exHarmony huggingface dataset:
Geometric Losses for Distributional Learning

Building upon recent advances in entropy-regularized optimal transport, and upon Fenchel duality between measures and continuous functions , we propose a generalization of the logistic loss that incorporates a metric or cost between classes. Unlike previous attempts to use optimal transport distances for learning, our loss results in unconstrained convex objective functions, supports infinite (or very large) class spaces, and naturally defines a geometric generalization of the softmax operator. The geometric properties of this loss make it suitable for predicting sparse and singular distributions, for instance supported on curves or hyper-surfaces. We study the theoretical properties of our loss and show-case its effectiveness on two applications: ordinal regression and drawing generation.
Handwritten Text Recognition and Digital Text Conversion

Sometimes it is extremely difficult to secure handwritten documents in the real world.While doing so, we may encounter many problems such as misplacing the documents, unavailability of access from anywhere, physical damage, etc.So, to keep the information secure, we convert that information into digital format to address all the above mentioned problems.The main aim of our application is to recognize hand written text and display it in digital text format.Image processing is very significant process for data analysis these days.In image processing, the visible text from the real world -as input-must be processed precisely in order to produce the same information -as output -with accuracy.To do this, the text present in the image must be recognized by the system accurately.The proposed system aims at achieving these results.The process goes in this way: The image which contains the handwritten text is fed to the system is passed into neural network which recognizes the handwritten text present in the image and displays it in the form of digital text.This can be used for many purposes such as copying the digital text for using it elsewhere, producing formal documents and can also be used as input for data processing.Using this process, we can store the information in a secure way, we can access the information from anywhere or at any time and there is no scope for physical damage as the information is in digital format.
Chat-Bot Based Natural Language Interface for Blogs and Information Networks

Chat-bot is a computer code that interacts with humans by itself without taking any human assistance. The chat-bot uses natural language processing (NLP) for understanding a human request or query. Blog is an information network where people (group) post information in their area of expertise and people with similar interests read and learn from them. The problem with traditional blog websites is that the user can only get the data which is linked to the home page and also the user have to manually go to every page to get the data. The proposed research work aims in designing and building a chat-bot which, with which the administrators and any common man can interact in English and ask for questions like 'show all the posts by abc', which hence provides a natural language (NL) interface between user's language (English) and the blog's (an information network) database.
Learning Context Graph for Person Search

Person re-identification has achieved great progress with deep convolutional neural networks. However, most previous methods focus on learning individual appearance feature embedding, and it is hard for the models to handle difficult situations with different illumination, large pose variance and occlusion. In this work, we take a step further and consider employing context information for person search. For a probe-gallery pair, we first propose a contextual instance expansion module, which employs a relative attention module to search and filter useful context information in the scene. We also build a graph learning framework to effectively employ context pairs to update target similarity. These two modules are built on top of a joint detection and instance feature learning framework, which improves the discriminativeness of the learned features. The proposed framework achieves state-of-the-art performance on two widely used person search datasets.
An Adversarial Risk Analysis Framework for Cybersecurity

Cyber threats affect all kinds of organisations. Risk analysis is an essential methodology for cybersecurity as it allows organisations to deal with the cyber threats potentially affecting them, prioritise the defence of their assets and decide what security controls should be implemented. Many risk analysis methods are present in cybersecurity models, compliance frameworks and international standards. However, most of them employ risk matrices, which suffer shortcomings that may lead to suboptimal resource allocations. We propose a comprehensive framework for cybersecurity risk analysis, covering the presence of both adversarial and non-intentional threats and the use of insurance as part of the security portfolio. A case study illustrating the proposed framework is presented, serving as template for more complex cases.
Two-round Multiparty Secure Computation from Minimal Assumptions

We provide new two-round multiparty secure computation (MPC) protocols in the dishonest majority setting assuming the minimal assumption that two-round oblivious transfer (OT) exists. If the assumed two-round OT protocol is secure against semi-honest adversaries (in the plain model) then so is our two-round MPC protocol. Similarly, if the assumed two-round OT protocol is secure against malicious adversaries (in the common random/reference string model) then so is our two-round MPC protocol. Previously, two-round MPC protocols were only known under relatively stronger computational assumptions.
Analysis of the use of Raspberry single-circuit computers in the teaching of distributed and parallel computing

The article considers the possibility of using single-board computers in teaching disciplines related to parallel and distributed computing. To conduct practical training in parallel computing, it is a very personal computer with a multi-core processor and an appropriate operating system, but for computational computing practice it is necessary to organize a computing cluster. To develop a cluster, you need a specific software setup that can make it difficult to use a computer class to teach other disciplines. Also, the purchase of personal computers for a computing cluster can lead to significant material costs. Therefore, it is expedient to explore the possibility of using single-board computers to develop a computing cluster and use it in practical work on distributed computing. The analysis of modern single-board computers and various sources for the development of computing clusters has shown that it is suitable for the single-board computer Raspberry Pi 3 Model B +. A comparative analysis of several Raspberry Pi models and, for comparison, one-board computer of another manufacturer – ASUS Tinker Board. It is revealed that Raspberry Pi 3 Model B + characteristics are optimal in the price / quality ratio for forming a learning computing cluster for distributed and parallel computing. Such a cluster has a relatively high price and very good scalability. Also, the problems that need to be solved when designing a computing cluster are found: there is no standardized chassis for placement of boards, the absence of standard heat removal from single-board computers and the need for a non-standard protected power supply. The organization of such a cluster will greatly improve students' skills in developing distributed computing software and build and configure the cluster systems and will allow the development of such competencies of future programmers. Key words. Parallel and distributed computing, single-board computer, computing cluster, Raspberry Pi, software.
Improving Safety in Reinforcement Learning Using Model-Based Architectures and Human Intervention

Recent progress in AI and Reinforcement learning has shown great success in solving complex problems with high dimensional state spaces. However, most of these successes have been primarily in simulated environments where failure is of little or no consequence. Most real-world applications, however, require training solutions that are safe to operate as catastrophic failures are inadmissible especially when there is human interaction involved. Currently, Safe RL systems use human oversight during training and exploration in order to make sure the RL agent does not go into a catastrophic state. These methods require a large amount of human labor and it is very difficult to scale up. We present a hybrid method for reducing the human intervention time by combining model-based approaches and training a supervised learner to improve sample efficiency while also ensuring safety. We evaluate these methods on various grid-world environments using both standard and visual representations and show that our approach achieves better performance in terms of sample efficiency, number of catastrophic states reached as well as overall task performance compared to traditional model-free approaches
Automatic learner summary assessment for reading comprehension

Automating the assessment of learner summaries provides a useful tool for assessing learner reading comprehension. We present a summarization task for evaluating non-native reading comprehension and propose three novel approaches to automatically assess the learner summaries. We evaluate our models on two datasets we created and show that our models outperform traditional approaches that rely on exact word match on this task. Our best model produces quality assessments close to professional examiners.
Online Multi-Object Tracking with Dual Matching Attention Networks

In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework to handle noisy detections and frequent interactions between targets. Specifically, for applying single object tracking in MOT, we introduce a cost-sensitive tracking loss based on the state-of-the-art visual tracker, which encourages the model to focus on hard negative distractors during online learning. For data association, we propose Dual Matching Attention Networks (DMAN) with both spatial and temporal attention mechanisms. The spatial attention module generates dual attention maps which enable the network to focus on the matching patterns of the input image pair, while the temporal attention module adaptively allocates different levels of attention to different samples in the tracklet to suppress noisy observations. Experimental results on the MOT benchmark datasets show that the proposed algorithm performs favorably against both online and offline trackers in terms of identity-preserving metrics.
Analysis of Digital Textbooks and the Improvements on the Development Process of Digital Textbooks from the Universal Design for Learning Perspective

The purpose of the study is to analyze the digital textbooks currently utilized in the educational field and to identify future improvements on the process of the digital textbook developments through developers’ input from the Universal Design for Learning (UDL) perspective. To achieve the objective, digital textbooks (grades 3–4 social studies and science subjects) developed based on the 2015 revised curriculum were analyzed based on the UDL principles. Additionally, the focus group interviews (FGI) were conducted with seven developers who had an experience on developing digital textbooks. First of all, from the analysis of digital textbooks, it was revealed that the level of UDL applicability in current digital textbooks is very low, thus the improvements on the digital textbooks were suggested for the following principle: options for perception, for physical action, for executive functions, and for sustaining effort and persistence. Furthermore, based on the findings from the FGI, improvements on the process of the digital textbook developments were suggested as follows: Time and budget support, development of technical standards, national pilot projects and usability testing of students with disabilities, providing Cloud-based LMS service, improving accessibility of platforms, provision of high-quality equipment and internet at the school site, and continuous management. Based on the results, we discussed and suggested further studies.
Ranking of Potential Questions

Questions are an integral part of discourse.They provide structure and support the exchange of information.One linguistic theory, the Questions Under Discussion model, takes question structures as integral to the functioning of a coherent discourse.This theory has not been tested on the count of its validity for predicting observations in real dialogue data though.In the present study, a system for ranking explicit and implicit questions by their appropriateness in a dialogue is presented.This system implements constraints and principles put forward in the linguistic literature.
Similarity Learning via Kernel Preserving Embedding

Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning of similarity information from data via self-expression has been developed and successfully applied in various models, such as low-rank representation, sparse subspace learning, semi-supervised learning. However, it just tries to reconstruct the original data and some valuable information, e.g., the manifold structure, is largely ignored. In this paper, we argue that it is beneficial to preserve the overall relations when we extract similarity information. Specifically, we propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work. Taking the clustering task as an example to evaluate our method, we observe considerable improvements compared to other state-of-the-art methods. More importantly, our proposed framework is very general and provides a novel and fundamental building block for many other similarity-based tasks. Besides, our proposed kernel preserving opens up a large number of possibilities to embed high-dimensional data into low-dimensional space.
A Survey of Optimization Methods from a Machine Learning Perspective

Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more challenges. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively. The systematic retrospect and summary of the optimization methods from the perspective of machine learning are of great significance, which can offer guidance for both developments of optimization and machine learning research. In this paper, we first describe the optimization problems in machine learning. Then, we introduce the principles and progresses of commonly used optimization methods. Next, we summarize the applications and developments of optimization methods in some popular machine learning fields. Finally, we explore and give some challenges and open problems for the optimization in machine learning.
Less is More: Learning Highlight Detection from Video Duration

Highlight detection has the potential to significantly ease video browsing, but existing methods often suffer from expensive supervision requirements, where human viewers must manually identify highlights in training videos. We propose a scalable unsupervised solution that exploits video duration as an implicit supervision signal. Our key insight is that video segments from shorter user-generated videos are more likely to be highlights than those from longer videos, since users tend to be more selective about the content when capturing shorter videos. Leveraging this insight, we introduce a novel ranking framework that prefers segments from shorter videos, while properly accounting for the inherent noise in the (unlabeled) training data. We use it to train a highlight detector with 10M hashtagged Instagram videos. In experiments on two challenging public video highlight detection benchmarks, our method substantially improves the state-of-the-art for unsupervised highlight detection.
The distribution of information for sEMG signals in the rectal cancer treatment process

The electrical activity of external anal sphincter can be registered with surface electromyography. This signals are known to be highly complex and nonlinear. This work aims in characterisation of the information carried in the signals by harvesting the concept of information entropy. We will focus of two classical measures of the complexity. Firstly the Shannon entropy is addressed. It is related to the probability spectrum of the possible states. Secondly the Spectral entropy is described, as a simple frequency-domain analog of the time-domain Shannon characteristics. We discuss the power spectra for separate time scales and present the characteristics which can represent the dynamics of electrical activity of this specific muscle group. We find that the rest and maximum contraction states represent rather different spectral characteristic of entropy, with close-to-normal contraction and negatively skewed rest state.
Unsupervised Bilingual Lexicon Induction from Mono-lingual Multimodal Data

Bilingual lexicon induction, translating words from the source language to the target language, is a long-standing natural language processing task. Recent endeavors prove that it is promising to employ images as pivot to learn the lexicon induction without reliance on parallel corpora. However, these vision-based approaches simply associate words with entire images, which are constrained to translate concrete words and require object-centered images. We humans can understand words better when they are within a sentence with context. Therefore, in this paper, we propose to utilize images and their associated captions to address the limitations of previous approaches. We propose a multi-lingual caption model trained with different mono-lingual multimodal data to map words in different languages into joint spaces. Two types of word representation are induced from the multi-lingual caption model: linguistic features and localized visual features. The linguistic feature is learned from the sentence contexts with visual semantic constraints, which is beneficial to learn translation for words that are less visual-relevant. The localized visual feature is attended to the region in the image that correlates to the word, so that it alleviates the image restriction for salient visual representation. The two types of features are complementary for word translation. Experimental results on multiple language pairs demonstrate the effectiveness of our proposed method, which substantially outperforms previous vision-based approaches without using any parallel sentences or supervision of seed word pairs.
PROBLEM BASED LEARNING (PBL) – QR-CODE DALAM PENINGKATAN HASIL BELAJAR MATEMATIKA PESERTA DIDIK

The purpose of this research is to improve students mathematics learning outcome through Problem Based Learning (PBL) with QR-Code on MIPA class grade X SMA Negeri 1 Kotagajah (Senior High School in Kotagajah). This research is an Action Research Class. The subject of this research is 36 Students. This research is cyclus research with Kemmis and Mc Taggart Models consisting of: (1) planning; (2) implementation; (3 observation; and (4) reflection. Data collection technique is using in this research is tes. Based on the research result, then obtained the conclusion is Problem Based Learning with QR-code can improve students mathematics learning outcome on MIPA Class grade X SMA Negeri 1 Kotagajah. This matter showed a percentage value on the first cyclus is 47,22% and increase on the second cyclus to be 63,89%.
Crowdsourcing in developing repository of phrase definition in Bahasa Indonesia

Language repository is valuable as a reference in using the language, its preservation, and in developing and implementation of natural language processing algorithms. Bahasa Indonesia is one of natural languages that hardly has repository despite its large number of speakers and previous attempts to build ones. We devised a way to develop repository of phrase definition in Bahasa using a kind of crowdsourcing and investigated its implementation. An application add-on was inserted to an information system that manages final year projects of undergraduate students. The add-on invites students to participate in writing keyword definition and validating definition. Investigation in a period of six months reveals that about 25% of application users take parts into the voluntary activities either as definition writers and/or validators. During the period, about 1200 phrase definitions were added into the repository and in average each definition is validated by two participants. The activity is supported by users that are well aware of the tasks, and have positive perception about the work, despite different reasons that motivate their contribution.
Exemplar Guided Face Image Super-Resolution without Facial Landmarks

Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images. Therefore, for super-resolving a given very low-resolution (LR) face image of a person it is very likely to find another HR face image of the same person which can be used to guide the process. In this paper, we propose a convolutional neural network (CNN)-based solution, namely GWAInet, which applies super-resolution (SR) by a factor 8x on face images guided by another unconstrained HR face image of the same person with possible differences in age, expression, pose or size. GWAInet is trained in an adversarial generative manner to produce the desired high quality perceptual image results. The utilization of the HR guiding image is realized via the use of a warper subnetwork that aligns its contents to the input image and the use of a feature fusion chain for the extracted features from the warped guiding image and the input image. In training, the identity loss further helps in preserving the identity related features by minimizing the distance between the embedding vectors of SR and HR ground truth images. Contrary to the current state-of-the-art in face super-resolution, our method does not require facial landmark points for its training, which helps its robustness and allows it to produce fine details also for the surrounding face region in a uniform manner. Our method GWAInet produces photo-realistic images in upscaling factor 8x and outperforms state-of-the-art in quantitative terms and perceptual quality.
Layered Image Compression using Scalable Auto-encoder

This paper presents a novel convolutional neural network (CNN) based image compression framework via scalable auto-encoder (SAE). Specifically, our SAE based deep image codec consists of hierarchical coding layers, each of which is an end-to-end optimized auto-encoder. The coarse image content and texture are encoded through the first (base) layer while the consecutive (enhance) layers iteratively code the pixel-level reconstruction errors between the original and former reconstructed images. The proposed SAE structure alleviates the need to train multiple models for different bit-rate points by recently proposed auto-encoder based codecs. The SAE layers can be combined to realize multiple rate points, or to produce a scalable stream. The proposed method has similar rate-distortion performance in the low-to-medium rate range as the state-of-the-art CNN based image codec (which uses different optimized networks to realize different bit rates) over a standard public image dataset. Furthermore, the proposed codec generates better perceptual quality in this bit rate range.