Reviewerly/exHarmony huggingface dataset:
Source Code Level Word Embeddings in Aiding Semantic Test-to-Code Traceability

Proper recovery of test-to-code traceability links from source code could considerably aid software maintenance. Scientific research has already shown that this can be achieved to an extent with a range of techniques relying on various information sources. This includes information retrieval which considers the natural language aspects of the source code. Latent Semantic Indexing (LSI) is widely looked upon as the mainstream technique of this approach. Techniques utilizing word embedding information however also use similar data and nowadays enjoy immense popularity in several fields of study. In this work, we present our evaluation of both LSI and word embeddings in aiding class level test-to-code traceability of 4 open source software systems, the assessment relying on naming convention information.
Image Captioning with Attribute Refinement

Semantic attention has long been adopted to image captioning models to enhance the image captioning performances. The models pre-trained for attribute recognition are utilized to generate image attributes in image captioning. Generally, these models are not jointly trained with image captioning models. In this paper, we propose attribute refinement network, which incorporates attribute recognition with image captioning to boost the performance on both tasks. We model the correlation between attributes with the semantic information from image captioning to improve the recognition accuracy. In turn, better attribute recognition results effectively enhance image captioning performance. Our model achieves CIDEr-D/SPICE scores of 115.1 and 20.9 respectively on the MS COCO test set, comprehensively yields improvement over all compared methods.
Action Coherence Network for Weakly Supervised Temporal Action Localization

Most prominent temporal action localization methods are of the fully-supervised type, which rely heavily on frame-level labels, which could be prohibitively expensive to annotate. Thanks to recent developments on the Weakly-supervised Temporal Action Localization (W-TAL), this alternative paradigm requires only video-level labels in training, alleviating such annotation efforts. Specifically, we present Action Coherence Network (ACN) for W-TAL, which features a new coherence loss that better supervises action boundary learning and facilitate proposal regression. In addition, a purpose-built fusion module is proposed for localization inference based on features extracted by two streams of convolutional neural network. Overall, the proposed ACN achieves state-of-the-art W-TAL performance on two challenging datasets (THU-MOS14 and ActivityNet1.2, particularly ACN attains mAP of 24.2% on THUMOS14 under IoU threshold 0.5), which is approaching some recent fully-supervised TAL methods.
Label Embedding using Hierarchical Structure of Labels for Twitter Classification

Twitter is used for various applications such as disaster monitoring and news material gathering.In these applications, each Tweet is classified into pre-defined classes.These classes have a semantic relationship with each other and can be classified into a hierarchical structure, which is regarded as important information.Label texts of pre-defined classes themselves also include important clues for classification.Therefore, we propose a method that can consider the hierarchical structure of labels and label texts themselves.We conducted evaluation over the Text REtrieval Conference (TREC) 2018 Incident Streams (IS) track dataset, and we found that our method outperformed the methods of the conference participants.
Mangan: Assisting Colorization Of Manga Characters Concept Art Using Conditional GAN

Colorization is a challenging task that has recently been tackled by deep learning. Line art colorization is particularly difficult because there is no grayscale value to indicate the color intensities as there is in black-and-white photograph images. When designing a character, concept artists often need to try different color schemes, however, colorization is a time-consuming task. In this article, we propose a semi-automatic framework for colorizing manga concept arts by letting concept artists try different color schemes and obtain colorized results in fashion time. Our approach uses Conditional Generative Adversarial Networks (cGAN) and outperforms current hint-based line-art colorization techniques by providing natural-looking arts with only minor coloring mistakes.
Live software development

Live Programming is an idea pioneered by programming environments from the earliest days of computing, such as those for Lisp and Smalltalk. One thing they had in common is liveness: an always accessible evaluation and nearly instantaneous feedback, usually focused on coding activities. In this paper, we argue for Live Software Development (LiveSD), bringing liveness to software development activities beyond coding, to make software easier to visualize, simpler to understand, and faster to evolve. Multiple challenges may vary with the activity and application domain. Research on this topic needs to consider the more important liveness gaps in software development, which representations and abstractions better support developers, and which tools are needed to support it.
High-Fidelity Face Sketch-To-Photo Synthesis Using Generative Adversarial Network

Face sketch-photo synthesis has important usage in law enforcement and human authentication. Due to the sparse information (no color or texture), the abstraction level, the diversity of sketches, and the domain gap between sketch and photo, it is challenging to synthesize a photo-realistic photo from an input sketch. Moreover, the deficiency of data also restricts the synthesis performance. In this paper, we present a high-fidelity face sketch-photo synthesis method using Generation Adversarial Network (GAN). Our network adopts a deep residual U-Net as generator and a Patch-GAN with residual blocks as discriminator. We design effective loss functions by enforcing pixels, edges and high-level features of the produced face photos. Moreover, we augment the CUHK sketch dataset using an effective sampling method. With the improved GAN and augmented dataset, we achieve high-fidelity face photos. Qualitative and quantitative experiments demonstrate the approach outperforms other method. Further experiments with a sketch-based photo editing application also validate the performance of our method.
Federated AI for the Enterprise: A Web Services Based Implementation

Many enterprise solutions can greatly benefit from Machine Learning (ML) models that are created from cross-domain enterprise data. However, many enterprises cannot share data freely across different locations due to regulatory restrictions, performance issues in moving large data volumes, or requirements to maintain autonomy. In such situations, the enterprise can benefit from the concept of federated learning in which ML models are created at multiple different geographic sites. These are combined together at a federation server without the need to share data. Motivated by the fact that web-services based architectures provide a means for robust integration of cross-domain information, in this paper, we describe a solution to the federated learning problem using such an architecture. We specifically focus on the problems enterprises encounter in using distributed data and discuss how we solved those problems through the solution architecture.
Exploring Factors for Improving Low Resolution Face Recognition

State-of-the-art deep face recognition approaches report near perfect performance on popular benchmarks, e.g., Labeled Faces in the Wild. However, their performance deteriorates significantly when they are applied on low quality images, such as those acquired by surveillance cameras. A further challenge for low resolution face recognition for surveillance applications is the matching of recorded low resolution probe face images with high resolution reference images, which could be the case in watchlist scenarios. In this paper, we have addressed these problems and investigated the factors that would contribute to the identification performance of the state-of-the-art deep face recognition models when they are applied to low resolution face recognition under mismatched conditions. We have observed that the following factors affect performance in a positive way: appearance variety and resolution distribution of the training dataset, resolution matching between the gallery and probe images, and the amount of information included in the probe images. By leveraging this information, we have utilized deep face models trained on MS-Celeb-1M and fine-tuned on VGGFace2 dataset and achieved state-of-the-art accuracies on the SCFace and ICB-RW benchmarks, even without using any training data from the datasets of these benchmarks.
Retracted: Measurement of Cholesterol Conditions of Eye Image using Fuzzy Local Binary Pattern (FLBP) and Linear Regression

Cholesterol is a complex fat compound, which is produced from the body and food substances. Cholesterol is needed by the body for the formation of cell walls and as a raw material for several hormones. But if the cholesterol content in the blood is excessive, it will cause the disease. To find out cholesterol levels in the blood, a laboratory check is usually done by taking blood samples. However, in the world of iridology, a technique for analyzing disease and weakness in the body is based on the shape and structure of the iris. One of the diseases that can be analyzed through the iris is cholesterol. Cholesterol, through the iris is marked by a change in iris pattern called Arcus Senilis. To see the cholesterol ring in the eye is not easy, because everyone has a different iris structure so that it becomes a characteristic of identification with someone. In this research, a system for measuring cholesterol levels through eye images has been designed so that it can facilitate a person in detecting cholesterol levels early. The process to be carried out starts from taking 60 training eye images and 30 test eye images using a mobile camera. Then the results of the photograph will be carried out using the feature extraction using FLBP and obtain the best FLBP operator there is sampling point 8 and radius 4 with F=7. After knowing the characteristic value, it was analyzed using linear regression to obtain measurement modeling. The results of the measurement model will be used to determine cholesterol levels. The results in this study are still quite large at 38.28 with a computational time of 11 seconds for each test image.
A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Cameras

We describe in this paper a Two-Stream Siamese Neural Network for vehicle re-identification. The proposed network is fed simultaneously with small coarse patches of the vehicle shape's, with 96 × 96 pixels, in one stream, and fine features extracted from license plate patches, easily readable by humans, with 96 × 48 pixels, in the other one. Then, we combined the strengths of both streams by merging the Siamese distance descriptors with a sequence of fully connected layers, as an attempt to tackle a major problem in the field, false alarms caused by a huge number of car design and models with nearly the same appearance or by similar license plate strings. In our experiments, with 2 hours of videos containing 2982 vehicles, extracted from two low-cost cameras in the same roadway, 546 ft away, we achieved a F-measure and accuracy of 92.6% and 98.7%, respectively. We show that our network, available at https://github.com/icarofua/siamese-two-stream, outperforms other One-Stream architectures, even if they use higher resolution image features.
A Real-Time Early Warning Seismic Event Detection Algorithm Using Smart Geo-Spatial Bi-Axial Inclinometer Nodes for Industry 4.0 Applications

Earthquakes are one of the major natural calamities as well as a prime subject of interest for seismologists, state agencies, and ground motion instrumentation scientists. The real-time data analysis of multi-sensor instrumentation is a valuable knowledge repository for real-time early warning and trustworthy seismic events detection. In this work, an early warning in the first 1 micro-second and seismic wave detection in the first 1.7 milliseconds after event initialization is proposed using a seismic wave event detection algorithm (SWEDA). The SWEDA with nine low-computation-cost operations is being proposed for smart geospatial bi-axial inclinometer nodes (SGBINs) also utilized in structural health monitoring systems. SWEDA detects four types of seismic waves, i.e., primary (P) or compression, secondary (S) or shear, Love (L), and Rayleigh (R) waves using time and frequency domain parameters mapped on a 2D mapping interpretation scheme. The SWEDA proved automated heterogeneous surface adaptability, multi-clustered sensing, ubiquitous monitoring with dynamic Savitzky–Golay filtering and detection using nine optimized sequential and structured event characterization techniques. Furthermore, situation-conscious (context-aware) and automated computation of short-time average over long-time average (STA/LTA) triggering parameters by peak-detection and run-time scaling arrays with manual computation support were achieved.
A privacy-preserving distributed filtering framework for NLP artifacts

Medical data sharing is a big challenge in biomedicine, which often hinders collaborative research. Due to privacy concerns, clinical notes cannot be directly shared. A lot of efforts have been dedicated to de-identifying clinical notes but it is still very challenging to accurately locate and scrub all sensitive elements from notes in an automatic manner. An alternative approach is to remove sentences that might contain sensitive terms related to personal information.A previous study introduced a frequency-based filtering approach that removes sentences containing low frequency bigrams to improve the privacy protection without significantly decreasing the utility. Our work extends this method to consider clinical notes from distributed sources with security and privacy considerations. We developed a novel secure protocol based on private set intersection and secure thresholding to identify uncommon and low-frequency terms, which can be used to guide sentence filtering.As the computational cost of our proposed framework mostly depends on the cardinality of the intersection of the sets and the number of data owners, we evaluated the framework in terms of these two factors. Experimental results demonstrate that our proposed method is scalable in various experimental settings. In addition, we evaluated our framework in terms of data utility. This evaluation shows that the proposed method is able to retain enough information for data analysis.This work demonstrates the feasibility of using homomorphic encryption to develop a secure and efficient multi-party protocol.
InDexMo

This work explores and evaluates the designs of finger-worn radio-frequency identification (RFID) motion tracking for activity recognition on tagged objects. We propose an index-finger-worn device that consists of a short-range (~2cm) RFID reader and a pair of two inertial measurement units (IMUs), which are mounted at the locations where an artificial nail and a ring are worn. The short-range RFID reader recognizes the tagged object on finger touch, and then the IMU data are used for activity recognition. Data collected from the user of this device allows for a post-hoc analysis, which informs the activity recognition performance in various RFID+IMU and IMU-only configurations on the same task. The results of a ten-participant user study show that when the objects have similar physical form factors, the hybrid RFID motion tracking significantly outperforms the IMU-only tracking, especially in a larger-number set of objects. In our test, three IMU configurations (i.e., NailOnly, RingOnly, and Nail+Ring) achieved comparable action recognition performances, i.e., ≥90% accuracy, with 500 ms recognition time, though the NailOnly RFID+IMU configuration provided the highest wearability. The practical challenges toward a real-world deployment of a finger-worn RFID motion tracking system are also discussed.
Early author profiling on Twitter using profile features with multi-resolution

The Author Profiling (AP) task aims to predict demographic characteristics about the authors from documents (e.g., age, gender, native language). The research so far has focused only on forensic scenarios by performing post-analysis using all the available text evidence. This paper introduces the task of Early Author Profiling (EAP) in Twitter. The goal is to effectively recognize profiles using as few tweets as possible from the user history. The task is highly relevant to support social media analysis and different problems related to security and marketing, where prevention and anticipation is crucial. This work proposes a novel strategy that combines a state of the art representation for early text classification and specialized word-vectors for author profiling tasks. In this strategy we build prototypical features called Profile based Meta-Words, which allow us to model AP information at different levels of granularity. Our evaluation shows that the proposed methodology is well suited for profiling little text evidence (e.g., a handful of tweets) in early stages, but as more tweets become available other granularities better encode larger amounts of text in late stages. We evaluated the proposed ideas on gender and language variety identification for English and Spanish, and showed that the proposal outperforms state of the art methodologies.
Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)

Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services. In addition to financial losses incurred, patients who genuinely need medical care suffer because service providers are not paid on time as a result of delays in the manual vetting of their claims and are therefore unwilling to continue offering their services. Health insurance claims fraud is committed through service providers, insurance subscribers, and insurance companies. The need for the development of a decision support system (DSS) for accurate, automated claim processing to offset the attendant challenges faced by the National Health Insurance Scheme cannot be overstated. This paper utilized the National Health Insurance Scheme claims dataset obtained from hospitals in Ghana for detecting health insurance fraud and other anomalies. Genetic support vector machines (GSVMs), a novel hybridized data mining and statistical machine learning tool, which provide a set of sophisticated algorithms for the automatic detection of fraudulent claims in these health insurance databases are used. The experimental results have proven that the GSVM possessed better detection and classification performance when applied using SVM kernel classifiers. Three GSVM classifiers were evaluated and their results compared. Experimental results show a significant reduction in computational time on claims processing while increasing classification accuracy via the various SVM classifiers (linear (80.67%), polynomial (81.22%), and radial basis function (RBF) kernel (87.91%).
How to Use Heuristics for Differential Privacy

We develop theory for using heuristics to solve computationally hard problems in differential privacy. Heuristic approaches have enjoyed tremendous success in machine learning, for which performance can be empirically evaluated. However, privacy guarantees cannot be evaluated empirically, and must be proven --- without making heuristic assumptions. We show that learning problems over broad classes of functions --- those that have polynomially sized universal identification sets --- can be solved privately and efficiently, assuming the existence of a non-private oracle for solving the same problem. Our first algorithm yields a privacy guarantee that is contingent on the correctness of the oracle. We then give a reduction which applies to a class of heuristics which we call certifiable, which allows us to convert oracle-dependent privacy guarantees to worst-case privacy guarantee that hold even when the heuristic standing in for the oracle might fail in adversarial ways. Finally, we consider classes of functions for which both they and their dual classes have small universal identification sets. This includes most classes of simple boolean functions studied in the PAC learning literature, including conjunctions, disjunctions, parities, and discrete halfspaces. We show that there is an efficient algorithm for privately constructing synthetic data for any such class, given a non-private learning oracle. This in particular gives the first oracle-efficient algorithm for privately generating synthetic data for contingency tables. The most intriguing question left open by our work is whether or not every problem that can be solved differentially privately can be privately solved with an oracle-efficient algorithm. While we do not resolve this, we give a barrier result that suggests that any generic oracle-efficient reduction must fall outside of a natural class of algorithms (which includes the algorithms given in this paper).
A Detect-Then-Retrieve Model for Multi-Domain Fashion Item Retrieval

Street-to-Shop fashion item retrieval is an instance-level image retrieval task in which a photo from a user is used to query a fashion image database in order to retrieve either the same or similar fashion items. This task is particularly challenging due to the domain shift between database photos, which tend to be stages, professional shots, and consumer photos that have a much greater variety in terms of quality, pose, etc. To reduce the problem difficulty, state-of-the-art approaches train one retrieval model per domain or fashion item category. In this work we propose a single detect-then-retrieve model that can be applied to any (query or database) image and which outperforms methods using domain or category-specific retrieval models by significant margins on the Exact Street2Shop benchmark dataset.
Older adults eating together in a virtual living room: opportunities and limitations of eating in augmented virtuality

This study used augmented virtuality with an overall aim to prevent undernourishment among older adults. The purpose was to facilitate remote social eating for solitary older adults, as people tend to eat more when socializing. In this study, an augmented virtuality application was developed in which a physical cake buffet and a virtual living room coexisted and interacted in real time. This was possible using an Oculus Rift CV1 HMD with an Intel Realsense SR300 depth sensor mounted on top of the HMD. The study included initial workshops with 30 experts and 16 older adults, prototyping with 7 mobility-restricted older adults, and a final user study with 27 older adults. In the user study, we evaluated the user experience of a system designed to establish a meal conversation between three older friends while eating a solitary meal in augmented virtuality. Within three overall factors (user, context, and system), we outlined sub- elements that constituted both opportunities and limitations, which included interactions, perceptions, and behavior in the augmented virtuality. The virtual living room was described very positively by all participants. However, there were also some technological limitations in terms of fidelity, HMD comfort, and quality. When developing virtual environments, we found it very important to include very specific elements within the user, contextual and system categories, as well including qualitative methods throughout the entire design process.