Reviewerly/exHarmony huggingface dataset:
ROSY Application for Selecting R Packages that Perform ROC Analysis

The empirical ROC curve is a powerful statistical tool to evaluate the precision of tests in several fields of study. This is a two-dimensional plot where the horizontal and vertical axis represent false positive and true positive fraction respectively, also referred to as 1-specificity and sensitivity, where precision is evaluated through a summary index, the area under the curve (AUC). Several computer tools are used to perform this analysis one of which is the R environment, this is an open source and free to use environment that allows the creation of different packages designed to perform the same tasks in distinct ways often resulting in different customization and features often providing similar results. There is a need to explore these different packages to provide an experienced user with the simplest and most robust execution of a needed analysis. This work catalogued the different R packages capable of ROC analysis exploring their performance. A shiny web application is presented that serves as a repository allowing for efficient use of all of these packages.
Joint Statistical and Spatial Sparse Representation for Robust Image and Image-Set Classification

Recent image classification schemes, by learning deep features from large-scale dataset, have achieved the significantly better results comparing to classic feature-based approaches. However, there are still challenges in practice, such as classifying noisy image-set queries and training over limited-scale dataset. Instead of applying generic deep features, the model-based approaches can be more effective for robust image and image-set classification tasks, as we need various image priors to exploit the inter- and intra-set data variations while prevent over-fitting. In this work, we propose a novel joint statistical and spatial sparse representation, dubbed J3S, to model the image or image-set data, by exploiting both their local patch structures and global Gaussian distribution into Riemannian manifold. To the best of our knowledge, no work to date utilized both global statistics and local patch structures jointly via sparse representation. We propose to solve a co-regularized sparse coding problem based on the J3S model, by coupling the local and global representations using joint sparsity. The learned J3S models are used for robust image and image-set classification. Experiments show that the proposed J3S-based image classification scheme outperforms the popular or state-of-the-art competing methods.
Modeling Histological Patterns for Differential Diagnosis of Atypical Breast Lesions

Our goal in this paper is to build parametric models for a dictionary of histological patterns that aid in the differential diagnosis of atypical breast lesions and evaluate the inferential power of these hand-crafted features. Diagnosis of high-risk atypical breast lesions is challenging and remains a critical component of breast cancer screening, presenting even for experienced pathologists a more difficult classification problem than the binary detection task of cancer vs not-cancer. Following guidelines in the WHO classification of the tumors of the breast (an essential reference for pathologists, clinicians and researchers) and in consultation with our team of breast sub-specialists (N = 3), we assembled a visual dictionary of sixteen histological patterns (e.g., cribriform, picket-fence), a subset that pathologists frequently use in making complex diagnostic decisions of atypical breast lesions. We invoke parametric models for each pattern using a mix of unary, binary and ternary features that account for morphological and architectural tissue properties. We use 1441 ductal regions of interest (ROIs) extracted automatically from 93 whole slide images (WSIs) with a computational pathology pipeline. We collected diagnostic labels for all of the ROIs: normal and columnar cell changes (CCC) as low-risk benign lesions (= 1124), and flat epithelium atypia (FEA) and atypical ductal hyperplasia (ADH) as high-risk benign lesions (= 317). We generate likelihood maps for each dictionary pattern across a given ROI and integrate this information to determine a diagnostic label of high- or low-risk. Our method has comparable classification accuracies to the pool of breast pathology sub-specialists. Our study enables a deeper understanding of the discordance among pathologists in diagnosing atypical breast lesions.
Tracing Diagnosis Paths on Histopathology WSIs for Diagnostically Relevant Case Recommendation

Telepathology has enabled the remote cancer diagnosis based on digital pathological whole slide images (WSIs). During the diagnosis, the behavior information of the pathologist can be recorded by the platform and then archived with the digital cases. The diagnosis path of the pathologist on a WSI is valuable information since the image content within the path is highly correlated with the diagnosis report of the pathologist. In this paper, we proposed a novel diagnosis path network (DPathNet). DPathNet utilizes the diagnosis paths of pathologists on the WSIs as the supervision to learn the pathology knowledge from the image content. Based on the DPathNet, we develop a novel approach for computer-aided cancer diagnosis named session-based histopathology image recommendation (SHIR). SHIR summaries the information of a WSI while the pathologist browsing the WSI and actively recommends the relevant cases within similar image content from the database. The proposed approaches are evaluated on a gastric dataset containing 983 cases within 5 categories of gastric lesions. The experimental results have demonstrated the effectiveness of the DPathNet to the SHIR task and the supervision of the diagnosis path is sufficient to train the DPathNet. The MRR and MAP of the proposed SHIR framework are respectively 0.741 and 0.777 on the gastric dataset.
Terminology extraction in the field of water environment based on rules and statistics

The acquisition of terminology in the field of water environment is the key to constructing the ontology of related fields, and it is also an important part of information extraction and information retrieval. This paper proposes algorithms based on rules and statistics to extract water environmental terms. Firstly, use the N-gram algorithm to segment the pre-processed text. Secondly, use relevant rules to filter the vocabulary. And then, use improved mutual information and adjacency entropy to filter the vocabulary to obtain candidate term words. Finally, use TFIDF to select terms related to the field of water environment. Experiments show that this method has achieved good results in extracting terminology in the field of water environment.
Improved Polynomial Neural Networks with Normalised Activations

Polynomials, which are widely used to study non-linear systems, have been shown to be extremely useful in analyzing neural networks (NNs). However, the existing methods for training neural networks with polynomial activation functions (PAFs), called as PNNs, are applicable for shallow networks and give a stable performance with quadratic PAFs only. This is due to the optimization issues encountered during training PNNs. We propose a working model for PAFs using a novel normalizing transformation which alleviates the problem of training PNNs with arbitrary degree. Our PAF can be directly used to train shallow PNNs in practice for degrees as high as ten. It can also be utilized to learn multivariate sparse polynomials of small degrees. We also propose a way to train deep CNNs with PAFs which achieve performance similar to deep CNNs with standard activations. Through rigorous experimentation on multiple data sets, we show that PNNs can be effectively trained in practice. This also highlights the potential of the proposed method to support the research on using polynomials to study deep learning.
Removal Of Blocking Artifacts From JPEG-Compressed Images Using a Neural Network

The goal of this research was to develop a neural network that will improve the quality of JPEG compressed images, irrespective of compression level. After reviewing related articles, published papers, and previous works on developing a computationally efficient algorithm for reducing the blockiness and Gibbs oscillation artifacts in JPEG compressed images, we decided to integrate a neural network into a previously developed technique. For this approach, the Alphablend filter [35] was used to post process JPEG compressed images to reduce noise and artifacts. The Alphablend result was further improved upon by the application of a trained neural network. We compare our results with various other published works using post compression filtering methods.
Contextual Pattern Matching

The research on indexing repetitive string collections has focused on the same search problems used for regular string collections, though they can make little sense in this scenario. For example, the basic pattern matching query “list all the positions where pattern P appears” can produce huge outputs when P appears in an area shared by many documents. All those occurrences are essentially the same.In this paper we propose a new query that can be more appropriate in these collections, which we call contextual pattern matching. The basic query of this type gives, in addition to P, a context length \(\ell \), and asks to report the occurrences of all distinct strings XPY, with \(|X|=|Y|=\ell \). While this query is easily solved in optimal time and linear space, we focus on using space related to the repetitiveness of the text collection and present the first solution of this kind. Letting \(\overline{r}\) be the maximum of the number of runs in the BWT of the text T[1..n] and of its reverse, our structure uses \(O(\overline{r}\log (n/\overline{r}))\) space and finds the c contextual occurrences XPY of \((P,\ell )\) in time \(O(|P|\log \log n + c \log n)\). We give other space/time tradeoffs as well, for compressed and uncompressed indexes.
Stylised Image Generation From Deep Neural Networks

The purpose of convolutional neural networks is usually image classification but there are increasing studies attempting to reverse this common purpose in order to generate images. One of the most promising research directions is style transfer. This involves rendering the overall texture of an image into an artistic style. There are two common approaches in this field, which are feature representation based methods and generative adversarial network(GAN) based methods. In this paper, we focus on GAN based methods. We observed that most variants of GAN usually need paired data in order to generate the desired result, the training costs are very heavy and the quality of the result is not guaranteed. We propose an improved architecture for generative adversarial models for multi-style rendering. A new loss function configuration enables learning from unpaired data and generation of stylized images with specific artistic styles from normal photographs. A weighted combination of loss functions can control the trade-off between style and content of a stylized image.
Expert Locator System for Finding Co-authors Using Snowball Network Method: A Case Study of Fasilkom UI Lecturers

One of the essential activities for academics is research. Research is typically done through collaborations. One way to find research collaborators is through existing connections, for example, via the co-author relationships. However, existing systems only provide limited information on collaborators. Furthermore, previous works have not fully explored the details of co-author relationships. Hence, we propose an Expert Locator System (ELS) that can identify collaborators and provide information on the location, publication keywords, as well as their impact and productivity. We can obtain the relationship using the snowball network method, a type of Social Network Analysis (SNA). This ELS can serve to obtain collaborators' information that can be useful in conducting research.
Improving Efficiency of E-mail Classification Through On-Demand Spam Filtering

E-mail systems have been witnessing a race between e-mail security researchers and spammers; wherein modern spam filtering systems have been built that constantly evolve and use modern learning techniques to filter spam and likewise spammers explore tricks and ways that to a larger extent bypass these spam filters. Once messages from spammers enter into inbox of a user, they can pose a significant threat, even if subsequent spam messages from these spammers are blocked by the filter. These include activity monitoring of e-mail addresses and subsequent e-mail address harvesting, phishing, spread of viruses and strengthening botnets. This study through experiments explores some commercial as well as corporate e-mail systems and finds this vulnerability a reality. The paper proposes an on-demand spam filtering mechanism which allows an e-mail client to notify its users about the presence of spam messages in their inboxes and moves them to junk folders as a solution to this vulnerability. Experiments with the proposed on-demand filtering mechanism have demonstrated its effectiveness in removing spam messages from inboxes and improve classification efficiency to classify subsequent spam messages from these spammers as spam messages.
Relaxed Locality Preserving Supervised Discrete Hashing

Supervised discrete hashing (SDH) has widely attracted attention because it can directly obtain hash codes rather than relaxing the discrete constraint, which reduces information loss during the quantization process. However, SDH and its extensions tend to ignore the underlying geometry structure of data, while in many cases, data is lying on a low-dimensional manifold. Therefore, based on SDH, we propose a novel manifold learning-based supervised discrete hashing method in this paper to further improve the performance. Our goal is to learn compact hash codes by discovering latent information and using the local structure of data. To this end, we utilize the anchor graph to capture local neighborhood relationship embedding in data for manifold learning. Besides, since the previous methods use a strict binary label matrix for classification, we jointly optimize a relaxed label matrix to increase the discriminative ability of the proposed model. As such, the learned hash codes are more suitable for classification. Compared with SDH and other popular hashing methods, the experimental results on four large-scale datasets show that the proposed hashing method has better performance.
Secure CNN Accelerator

Deep learning techniques like Convolution Neural Networks (CNN) are nowadays very popular in applications like object detection, semantic segmentation etc. The differentiation among various CNN based solutions come mainly from training data, which gets translated to CNN model parameters. The traditional approach to secure the model by decoupling cryptography from inference has inherent disadvantages of higher boot time, possible DRAM snooping attack and complexity in working with multiple vendors on given SoC. This paper presents novel architecture to overcome these limitations by just-in-time decryption. The proposed solution uses novel techniques namely encryption of intermediate tensors, dynamic block chaining of tensors & weight and hardware key management. The proposed solution is designed and simulated for 28 nm silicon process node running at 600 MHz with synthesis area of ~1 mm2 confirming removal of mentioned limitations of traditional solutions.
Efficient and precise sensor fusion for non-linear systems with out-of-sequence measurements by example of mobile robotics

For most applications in mobile robotics, precise state estimation is essential. Typically, the state estimation is based on the fusion of data from different sensors. In practice, these sensors differ in their characteristics and measurements are available to the sensor fusion algorithm only with delay. Based on a brief survey of sensor fusion approaches that consider delayed and out-of-sequence availability of measurements, suitable approaches for applications in mobile robotics are identified. In a consumer robot use-case, experiments show that the estimation is biased if delayed availability of measurements is not considered appropriately. However, if delays are considered in the fusion process, the estimation bias is reduced to almost zero and in consequence, the estimation performance is distinctly improved. Two computational favorable approximative methods are described and provide almost the same accuracy as - theoretically optimal - brute-force filter recalculation at much lower and well-distributed computational costs.
Cross-Domain Adversarial Autoencoder for Fine Grained Category Preserving Image Translation

Cross-domain image translation attempt to translate images from one domain to another domain, with the content of images preserved. Current approaches treat image's content as the underlying spatial structure, and translation only change image's style of color and texture. These methods can generate realistic results, but may not be able to preserve image's fine grained semantic category information and suffer from the lack of diversity in objects' shapes and viewing angles. In this paper, we propose the problem of fine grained category preserving image translation that aims at preserving image's fine grained category information in cross-domain translation. A novel framework called Cross-Domain Adversarial AutoEncoder (CDAAE) is proposed to solve the problem. CDAAE assumes that cross-domain images have shared content-latent-code space and separate style-latent-code spaces. The content latent code encodes image's basic category information, while the style latent code represents other domain-specific properties, including color, texture, shape, etc. Our experiments evaluate models from aspects of image's quality, diversity as well as category preserving ability, showing CDAAE's advantages over current methods. We also design an algorithm to apply CDAAE to domain adaptation. Experiments on benchmark datasets demonstrate that the proposed method achieves state-of-the-art results.
Predicting Domain Specific Personal Attitudes and Sentiment

Microblog activity logs are useful to determine user’s interest and sentiment towards specific and broader category of events such as natural disaster and national election. In this paper, we present a corpus model to show how personal attitudes can be predicted from social media or microblog activities for a specific domain of events such as natural disasters. More specifically, given a user’s tweet and an event, the model is used to predict whether the user will be willing to help or show a positive attitude towards that event or similar events in the future. We present a new dataset related to a specific natural disaster event, i.e. Hurricane Harvey, that distinguishes user’s tweets into positive and non-positive attitudes. We build Term Embeddings for Tweet (TEmT) to generate features to model personal attitudes for arbitrary user’s tweets. In addition, we present sentiment analysis on the same disaster event dataset using enhanced feature learning on TEmT generated features by applying Convolutional Neural Network (CNN). Finally, we evaluate the effectiveness of our method by employing multiple classification techniques and comparative methods on the newly created dataset.
Connection Sparsification and Orbit Stabilization of Dynamic Binary Neural Networks based on Multiobjective Evolutionary Algorithms

0 A dynamic binary neural network is characterized by ternary connection parameters and can generate various bi-nary periodic orbits. This paper studies a two-objective problem for sparsity of the connection parameters and stability of the binary periodic orbits. In order to optimize the two-objective problem, we present a simple algorithm (ALG/M) based on the multiobjective evolutionary algorithm based on decomposition. The ALG/M decomposes the two-objective problem into multiple subproblems and can optimize the problem effectively. Performing elementary numerical experiments for typical examples of binary periodic orbits, it is confirmed that the ALG/M realizes both appropriate connection sparsity and strong orbit stability. It is also confirmed that the ALG/M outperforms another algorithm based on the regularization algorithms such as the Lasso.
Extrapolative-Interpolative Cycle-Consistency Learning For Video Frame Extrapolation

Video frame extrapolation is a task to predict future frames when the past frames are given. Unlike previous studies that usually have been focused on the design of modules or construction of networks, we propose a novel ExtrapolativeInterpolative Cycle (EIC) loss using pre-trained frame interpolation module to improve extrapolation performance. Cycle-consistency loss has been used for stable prediction between two function spaces in many visual tasks. We formulate this cycle-consistency using two mapping functions; frame extrapolation and interpolation. Since it is easier to predict intermediate frames than to predict future frames in terms of the object occlusion and motion uncertainty, interpolation module can give guidance signal effectively for training the extrapolation function. EIC loss can be applied to any existing extrapolation algorithms and guarantee consistent prediction in the short future as well as long future frames. Experimental results show that simply adding EIC loss to the existing baseline increases extrapolation performance on both UCF101 [1] and KITTI [2] datasets.
Distilling Essence of a Question: A Hierarchical Architecture for Question Quality in Community Question Answering Sites

Community question answering (CQA) sites have grown to be useful platforms where users search for highly specific information to resolve a problem. However, the significant increase in the number of user-generated content with high variance in quality on these sites not only presents challenges for user navigation but also outgrow the community's peer reviewing capacity. This necessitates ways to automatically assess the quality of new questions so as to maintain quality of content served to its users. While existing methods commonly employ social network indicators as features, our model aims to avoid social influence biases arising from these indicators by predicting the quality from semantic evaluation of the question text. Formulation of the proposed model is non-trivial as it requires the extraction of meaningful features from the noisy question text at different granularities while filtering redundant information. In this work, a neural architecture is proposed to address this problem by aggregating the textual features extracted at word- and sentence-level in a hierarchical manner. In addition, a unique attention mechanism that focuses on sentence segments for interpreting a question is developed. This new mechanism employs the global topical information from common problem contexts. The proposed approach is verified on the Stack Overflow question dataset and is shown to outperform existing neural models.