Reviewerly/exHarmony huggingface dataset:
On Approximating Metric Nearness Through Deep Learning

Many problems require a notion of distance between a set of points in a metric space, e.g., clustering data points in an N-dimensional space, object retrieval in pattern recognition, and image segmentation. However, these applications often require that the distances must be a metric, meaning that they must satisfy a set of conditions, with triangle inequality being the focus of this paper. Given an $$N \times N$$ dissimilarity matrix with triangle inequality violations, the metric nearness problem requires to find a closest distance matrix, which satisfies the triangle inequality condition. This paper introduces a new deep learning approach for approximating a nearest matrix with more efficient runtime complexity than existing algorithms. We have experimented with several deep learning architectures, and our experimental results demonstrate that deep neural networks can learn to construct a close-distance matrix efficiently by removing most of the triangular inequality violations.
Deep Residual Network with Self Attention Improves Person Re-Identification Accuracy

In this paper, we present an attention mechanism scheme to improve the person re-identification task. Inspired by biology, we propose Self Attention Grid (SAG) to discover the most informative parts from a high-resolution image using its internal representation. In particular, given an input image, the proposed model is fed with two copies of the same image and consists of two branches. The upper branch processes the high-resolution image and learns high dimensional feature representation while the lower branch processes the low-resolution image and learns a filtering attention grid. We apply a max filter operation to non-overlapping sub-regions on the high feature representation before element-wise multiplied with the output of the second branch. The feature maps of the second branch are subsequently weighted to reflect the importance of each patch of the grid using a softmax operation. Our attention module helps the network to learn the most discriminative visual features of multiple image regions and is specifically optimized to attend feature representation at different levels. Extensive experiments on three large-scale datasets show that our self-attention mechanism significantly improves the baseline model and outperforms various state-of-art models by a large margin.
Automatic machine learning for target recognition

Automatic Target Recognition (ATR) seeks to improve upon techniques from signal processing, pattern recognition (PR), and information fusion. Currently, there is interest to extend traditional ATR methods by employing Artificial Intelligence (AI) and Machine Learning (ML). In support of current opportunities, the paper discusses a methodology entitled: Systems Experimentation efficiency effectives Evaluation Networks (SEeeEN). ATR differs from PR in that ATR is a system deployment leveraging pattern recognition (PR) in a networked environment for mission decision making, while PR/ML is a statistical representation of patterns for classification. ATR analysis has long been part of the COMPrehensive Assessment of Sensor Exploitation (COMPASE) Center utilizing measures of performance (e.g., efficiency) and measures of effectiveness (e.g., robustness) for ATR evaluation. The paper highlights available multimodal data sets for Automated ML Target Recognition (AMLTR).
Age group recognition from face images using a fusion of CNN- and COSFIRE-based features

Automatic age group classification is the ability of an algorithm to classify face images into predetermined age groups. It is an important task due to its numerous applications such as monitoring, biometrics and commercial profiling. In this work we propose a fusion technique that combines CNN- and COSFIRE-based features for the recognition of age groups from face images. Both CNN and COSFIRE are trainable approaches that have been demonstrated to be effective in various computer vision applications. As to CNN, we use the pre-trained VGG-Face architecture and for COSFIRE we configure new COSFIRE filters from training data. Since recent literature suggests that CNNs deliver the highest accuracy rates within such problems, the hypothesis which we want to investigate in this work is whether combining CNN and COSFIRE approaches together will improve results. The proposed fusion technique using stacked Support Vector Machine (SVM) classifiers, and trained and tested with the FERET data set images has shown that, indeed, CNN- and COSFIRE-based features are complimentary as their combination reduces the error rate by more than 25%.
Pengaruh Metode Pembelajaran Ilearning Pada Kelas Independent Study Terhadap Peningkatan Kualitas Pembelajaran Dan Penilaian

Raharja College is one of the Universities that applies a learning method that is quite different, that is, it does not only rely on conventional learning systems where the KBM (Teaching and Learning Activities) is carried out by students and lecturers face-to-face, but also applying e-learning methods. learning or better known as the iLearning Education learning system at Raharja College. In its implementation, several devices are used to support the learning system starting from iDu (iLearning Education) as a media for interaction between Students and Lecturers as well as the Use of iLearning Media (IME) as a media that accommodates learning activities through the iLearning Education method. Then during the course of this method several questions arise about how valid the system is for the quality of the assessment and effectiveness of learning methods where it is deemed necessary to apply a measuring instrument that can measure system validity towards the quality and effectiveness of learning methods at Raharja College especially in the Independent class Study IL101A and IL101C. By using a descriptive method with a research survey approach conducted where the survey conducted found a problem solving that is the procurement of a measuring instrument that can later display historical data with certain categories in terms of the effectiveness of learning can be proven empirically through accurate data.
Data mining algorithm based on feature weighting

In order to overcome the defects of ESSC (Enhanced Soft Subspace Clustering), EWSC (Entropy Weighting Subspace Clustering) and FWSC (Fuzzy Weighting Subspace Clustering), a MOSSC (Multi-Objective Evolutionary-Based Soft Subspace Clustering) algorithm is proposed. Using multi-objective optimization technology, two objective functions of intra class and inter class in soft subspace clustering method are optimized respectively. Using the method of two-partitioning of weighted subspaces, the optimal solution set of non-dominant Pareto is analyzed. The final clustering results are derived. Then, experiments were designed to apply the MOSSC algorithm on artificial datasets and real datasets. The results show that the MOSSC algorithm has better performance than the soft subspace clustering algorithm and the multi-objective clustering method. The partition effect of MOSSC algorithm is better than that of soft subspace clustering algorithm.
Learning to Solve Single Variable Linear Equations by Universal Search with Probabilistic Program Graphs

The motivation for general AI is to overcome the problem of specificity in traditional AI approaches. Universal search guarantees to solve this problem with asymptotic optimality. However, the constant factor associated with the search time exponentially depends on solution size and can be immensely large. Evidently this reduces practical interest. Transfer learning can help reducing this constant factor for a series of related problems. We propose a dataflow graph-based programming model which evidently helps improving transfer learning between related tasks. We built a universal search based agent using our programming model which learned to solve single variable linear equations with minimal prior knowledge about list operations and arithmetic expression evaluation. The agent returned the solution as a program graph and was able to find general solution for a set of equations. Experimental results reveal the efficiency of the agent. The experiments demonstrate how universal search can be deployed in solving some practical problems like algebra equations.
Task-Adaptive Feature Reweighting for Few Shot Classification

Few shot classification remains a quite challenging problem due to lacking data to train an effective classifier. Lately a few works employ the meta learning schema to learn a generalized feature encoder or distance metric, which is directly used for those unseen classes. In these approaches, the feature representation of a class remains the same even in different tasks (In meta learning, a task of few shot classification involves a set of labeled examples (support set) and a set of unlabeled examples (query set) to be classified. The goal is to get a classifier for the classes in the support set.), i.e. the feature encoder cannot adapt to different tasks. As well known, when distinguishing a class from different classes, the most discriminative feature may be different. Following this intuition, this work proposes a task-adaptive feature reweighting strategy within the framework of recently proposed prototypical network [6]. By considering the relationship between classes in a task, our method generates a feature weight for each class to highlight those features that can better distinguish it from the rest ones. As a result, each class has its own specific feature weight, and this weight is adaptively different in different tasks. The proposed method is evaluated on two few shot classification benchmarks, miniImageNet and tieredImageNet. The experiment results show that our method outperforms the state-of-the-art works demonstrating its effectiveness.
WebRTC-Based Multi-View Video and Audio Transmission and its QoE

In this paper, we implement a Multi-View Video and Audio (MVV-A) transmission system utilizing WebRTC media channel, which employs UDP-based transmission into Web technologies, to enhance QoE under large delay. According to viewpoint change requests, this system switches audiovisual streams. We compare QoE with MVV-A transmission using MPEG-DASH, which employs HTTP/TCP, through a subjective experiment with various network conditions. As transmission methods utilizing MPEG-DASH, we treat single viewpoint transmission and simultaneous transmission of all the viewpoints. As a result, we find that the MVV-A transmission with WebRTC achieves higher QoE than that with MPEG-DASH under large delay.
Adversarial Learning for Visual Storytelling with Sense Group Partition

Visual storytelling aims to investigate the generation of a paragraph to describe the content of a photo stream. Despite the substantial progress in vision and language research, the techniques for sequential vision-to-language are still far away from being perfect. Due to the limitation of maximum likelihood estimation on training, the majority of existing models encourage high resemblance to texts in the training database, which makes the description overly rigid and lack in diverse expressions. Therefore, We cast the task as a reinforcement learning problem and propose an Adversarial All-in-one Learning (AAL) framework to learn a reward model, which simultaneously incorporates the information of all images in the photo stream and all texts in the paragraph, and optimize a generative model with the estimated reward. Specifically, in light of the linguistic reading theory with sense group as the unit, we propose to do the paragraph generation at sense group level instead of sentence level. Experiments on the widely-used dataset show that our approach generates higher-quality descriptions than previous baselines.
Segmentation image re-coloring based on k-means clustering algorithm as a tool for partial color-blind people

Color blindness is one of the eye anomalies that can not distinguish one color from another. Therefore, the information contained in the image may be lost seen by color blindness people. To reduce the possibility of sufferers losing color information contained in the image, a system was built in the form of re-coloring on images based on the ability of color blindness sufferers to distinguish colors. This ability is measured based on RGB cluster owned by different people. This research using color based segmentation in the process of re-coloring. Image Segmentation using K-means Clustering was chosen because of short processing time with optimum result. A total of 15 images without the coloring process were tested on color blind subjects. The result is that only 20% of the information can be seen by the color blind subject. The output of this study is the image that has been re-colored. The result of this research is that 53% of the images can be seen by the color-blind subject by using color-based segmentation in each image, 60% of the image can be seen by the color-blind subject by using color-based segmentation on all images, and processing time is shorter than previous research.
Team learning from human demonstration with coordination confidence

Abstract Among an array of techniques proposed to speed-up reinforcement learning (RL), learning from human demonstration has a proven record of success. A related technique, called Human-Agent Transfer, and its confidence-based derivatives have been successfully applied to single-agent RL. This article investigates their application to collaborative multi-agent RL problems. We show that a first-cut extension may leave room for improvement in some domains, and propose a new algorithm called coordination confidence (CC). CC analyzes the difference in perspectives between a human demonstrator (global view) and the learning agents (local view) and informs the agents’ action choices when the difference is critical and simply following the human demonstration can lead to miscoordination. We conduct experiments in three domains to investigate the performance of CC in comparison with relevant baselines.
HMMs for Unsupervised Vietnamese Word Segmentation

Word segmentation is an important problem in natural language processing. Most of previous works on Vietnamese word segmentation are supervised learning. In this paper, we propose an unsupervised method for Vietnamese word segmentation based on Hidden Markov Models. We naturally encode prior linguistic knowledge into model learning. In decoding, we propose an enhancement of Viterbi decoding algorithm with external token ordering statistics from Pointwise Mutual Information. Evaluation on benchmark datasets shows that the proposed method works reasonably well. Sourcecode is available at https://github.com/longbb/word_recognition
ExQuisiTe: Explaining Quantities in Text

Web pages and other documents often contain tables to provide numerical details in a structured manner. Typically, the text explains and highlights important quantities, often using approximate numbers and aggregates such as totals, averages or ratios. For a human reader, it is crucial to navigate between text and tables to understand the key information in its context, drill down into tables when details are needed, and obtain explanations on specific figures from the accompanying text.
Fast BCH syndrome generator using parallel polynomial division algorithm for GPGPUs

Recent developments in General Purpose Graphical Processing Units (GPGPU) has attracted many researchers to invent new methods to decode errors for linear block codes such as Bose-Chaudhuri-Hocquenghem (BCH), and Reed Solomon (RS) codes. In this paper, we propose a parallel polynomial division algorithm to compute the syndromes of a received BCH code. Further, using the parallel thread technique offered by the GPGPUs, we propose to use the same to generate the parity bits of the BCH encoder. We have devised an engine to compute the syndromes for BCH codes with field extension m=15, and we studied the GPU implementation of the same for bit errors t=2..15. The results, when compared against the conventional syndrome generator, has a minimum of 12% improvement in the efficiency of the syndrome generator kernel routines. Hence, this method provides an efficient way to compute syndromes for a programmable error correction mode.
A Context-aware Authentication Approach for Smartphones

Nowadays smartphones have become the most ubiquitous devices all over the world. People are becoming more dependent on smartphones in their daily lives. These devices provide access to several services at any time anywhere thanks to widespread availability of wireless network connectivity. A lot of private user's content (e.g. photos, videos, messages, etc.) and precious information (emails, files, etc.) are stored in smartphones. The pervasiveness of such devices raises new challenges to serious security risks and those devices should be protected against unauthorized access. Authentication is the most important step in the access control process which authorize the user to access his/her smartphone. Unfortunately, users perform the same authentication method to access to their smartphones in all context of use even though the method is not feasible or inappropriate to the current context. The aim of this paper is to propose an approach that provides smartphones users with the appropriate method for authentication according to the current context.
The Elastic k-Nearest Neighbours Classifier for Touch Screen Gestures

Touch screen gestures are a well-known method of person authentication in mobile devices. In most applications it is, however, reduced to checking if the user entered the correct pattern. Using additional information based on the speed and shape of finger movements can provide higher security without significantly impacting the convenience of this authorization method. In this work a new distance function for the k-nearest neighbour (kNN) classifier is considered in the problem of person recognition based on touch screen gestures. The function is based on the well-known $$\mathrm {L}^p$$ distance and the elastic distance considered in elastic shape analysis. Performance of the classifier is measured using 5-fold stratified cross-validation on a set of 12 people. Only four gesture performances per gesture for each person are used to train a model. The effects of sampling rate on the classifier performance is also measured. The kNN classifier with the proposed distance function has higher accuracy than both the $$\mathrm {L}^p$$ distance and the elastic distance.
Big Data and Safety Management Methods: The Reduction Model of Hot Work Number

The management of hot work is the key element of petroleum refinery safety management. A large amount of hot-work data has been accumulated, which is underexploited. Driving new insights using big data analytics is the trend. However, there have been few scientific studies on solving a specific problem using big data method in the field of safety management. Hence, the unstructured-data analysis of the hot-work permit-to-work text was investigated. The professional corpus in the hot-work field was constructed using word segmentation, stop list elimination, standardized process, and manual proofreading. All hot-work content collocates were manually grouped into semantic domains in the light of experts' experience. The data-driven reduction models of hot work number were proposed aiming at possible invalid hot work, long-time hot work, repetitive hot work and possible equipment defect. Based on the proposed reduction models, we mined the patterns of hot work and the unnecessary or high-risk hot work could be identified automatically. The result of the reduction model in training set indicated that the reduction models are reasonable. The reduction model 1, reduction model 2, reduction model 3, reduction model 4 could reduce the proportion of hot work number 5%, 3%, 4%, and 2%, respectively. Thus, the targeted measures could be put forward to optimize the safety management of hot work.
Risk bound of transfer learning using parametric feature mapping and its application to sparse coding

In this study, we consider a transfer-learning problem using the parameter transfer approach, in which a suitable parameter of feature mapping is learned through one task and applied to another objective task. We introduce the notion of local stability and parameter transfer learnability of parametric feature mapping, and derive an excess risk bound for parameter transfer algorithms. As an application of parameter transfer learning, we discuss the performance of sparse coding in self-taught learning. Although self-taught learning algorithms with a large volume of unlabeled data often show excellent empirical performance, their theoretical analysis has not yet been studied. In this paper, we also provide a theoretical excess risk bound for self-taught learning. In addition, we show that the results of numerical experiments agree with our theoretical analysis.
Person Entity Attribute Extraction Based on Siamese Network

Entity attribute extraction, which converts chaotic text data to structured knowledge, plays an important role in natural language processing (NLP). Many previous studies proved that the representation of text has a significant impact on the results of attribute extraction. In this paper, we propose a novel model to obtain the discriminative representation of sentences by applying the Siamese architecture. Specifically, we simultaneously input two variable-length sentences in the training stage of our model, namely, the main sentence and its similar or dissimilar partner. This scheme of pair is beneficial for entity attribute extraction. First, the entity attribute extraction community suffers the insufficient but expensive labeled data, the two input sets produce much more samples for the representation learning and can be treated as a useful data augmentation method. More importantly, the co-learning by the Siamese architecture achieves more interesting embedding than the separate way, since the informative relation between the focused sentence and its partner help the representation learning to explore more essential semantics and keep stable to the variation of wording and syntax of a sentence. The experiments on the Wikipedia data show that our model takes advantage of the Siamese architecture for sentence embedding and achieves significant improvements on attribute extraction as compared with baselines. To the best of our knowledge, we are the first to introduce the Siamese network into the person entity attribute extraction, which we proved to achieve the state of the art.
A Proposed Approach For Handling The Tradeoff Between Security, Usability, and Cost

The abuse of free web-based services has been raised in today's cyber world. In response, researchers have developed security mechanisms to protect users and system resources from misuse. However, attackers are increasingly finding new ways to avoid detection by these mechanisms. As a consequence, new and improved security mechanisms are in constant need of development, which may be a challenge in terms of cost. Increased security may also impair the usability of the web-based services. As such, there is often a clear trade-off between the variables of: security level, usability and cost. This paper therefore proposes approaches that can effectively handle this trade-off.