Reviewerly/exHarmony huggingface dataset:
Data Capsule: Representation of Heterogeneous Data in Cloud-Edge Computing

Nowadays, robots (including non-humanoid ones, like self-driving cars) are part of the most promising technologies, and they rise various computing requirements. Some of those requirements came from the fact that the robots are involved in highly dynamic environments and have to execute complex decision algorithms in real-time, while other requirements ask for batch processing of big data compatible datasets. In this paper, we propose a cloud architecture for optimizing data processing using a cloud-edge infrastructure. Besides the computational architecture, we develop a mathematical model for each type of entity in our proposal and a formal description of a data capsule, which represents a generic and flexible representation for unstructured units of data in time series databases. The architecture includes multiple processing platforms. We evaluate the proposed model in edge-cloud computing platforms designed for robots that run machine learning tasks.
Effects of Chinese character size, number of characters per line, and number of menu items on visual search task on tablet computer displays for different age groups

The goal of this study was to investigate the effects of Chinese character size, number of characters per line, and number of menu items on visual search task performance on a tablet computer for different age group users. Forty-two participants participated the visual search experiment. Gender, age group (younger, middle-aged, and older), Chinese character size (15, 17, 19, 21, 23, and 25 point), number of characters per line (2–5 characters, 9–14 characters, and 15–18 characters), and number of menu items (5, 7, 9, 11, and 13 items) were the independent variables. The dependent variables included search time and error rate. Gender had a large effect size on search time. Female participants had a faster search time than male participants. Significant differences and large effect sizes were found in search performance for different character sizes, number of characters per line, and number of menu items. Nine to fourteen characters per line achieved the fastest search time and lowest error rate. The fastest search time was for a 19-point character size in the younger group; 21-point character size in the middle-aged group; and 23-point character size in the older group. Besides, the fastest search time was found when showing 9 items in the younger group; 7 items in the middle-aged group; and 5 items in the older group. The optimum visual search performance was achieved with 19-point character size showing 9 items and 9–14 characters per line for the younger group; 21-point character size showing 7 items and 9–14 characters per line for the middle-aged group; and 23-point character size showing 5 items and 9–14 characters per line for the older group. The results provide useful information for tablet computer interface-presentations for different age group users.
Voice Presentation Attack Detection through Text-Converted Voice Command Analysis

Voice assistants are quickly being upgraded to support advanced, security-critical commands such as unlocking devices, checking emails, and making payments. In this paper, we explore the feasibility of using users' text-converted voice command utterances as classification features to help identify users' genuine commands, and detect suspicious commands. To maintain high detection accuracy, our approach starts with a globally trained attack detection model (immediately available for new users), and gradually switches to a user-specific model tailored to the utterance patterns of a target user. To evaluate accuracy, we used a real-world voice assistant dataset consisting of about 34.6 million voice commands collected from 2.6 million users. Our evaluation results show that this approach is capable of achieving about 3.4% equal error rate (EER), detecting 95.7% of attacks when an optimal threshold value is used. As for those who frequently use security-critical (attack-like) commands, we still achieve EER below 5%.
NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction

Biomedical knowledge bases are crucial in modern data-driven biomedical sciences, but auto-mated biomedical knowledge base construction remains challenging. In this paper, we consider the problem of disease entity normalization, an essential task in constructing a biomedical knowledge base. We present NormCo, a deep coherence model which considers the semantics of an entity mention, as well as the topical coherence of the mentions within a single document. NormCo mod-els entity mentions using a simple semantic model which composes phrase representations from word embeddings, and treats coherence as a disease concept co-mention sequence using an RNN rather than modeling the joint probability of all concepts in a document, which requires NP-hard inference. To overcome the issue of data sparsity, we used distantly supervised data and synthetic data generated from priors derived from the BioASQ dataset. Our experimental results show thatNormCo outperforms state-of-the-art baseline methods on two disease normalization corpora in terms of (1) prediction quality and (2) efficiency, and is at least as performant in terms of accuracy and F1 score on tagged documents.
Deep Co-Clustering

Co-clustering partitions instances and features simultaneously by leveraging the duality between them and it often yields impressive performance improvement over traditional clustering algorithms. The recent development in learning deep representations has demonstrated the advantage in extracting effective features. However, the research on leveraging deep learning frameworks for co-clustering is limited for two reasons: 1) current deep clustering approaches usually decouple feature learning and cluster assignment as two separate steps, which cannot yield the task-specific feature representation; 2) existing deep clustering approaches cannot learn representations for instances and features simultaneously. In this paper, we propose a deep learning model for co-clustering called DeepCC. DeepCC utilizes the deep autoencoder for dimension reduction, and employs a variant of Gaussian Mixture Model (GMM) to infer the cluster assignments. A mutual information loss is proposed to bridge the training of instances and features. DeepCC jointly optimizes the parameters of the deep autoencoder and the mixture model in an end-to-end fashion on both the instance and the feature spaces, which can help the deep autoencoder escape from local optima and the mixture model circumvent the Expectation-Maximization (EM) algorithm. To the best of our knowledge, DeepCC is the first deep learning model for co-clustering. Experimental results on various dataseis demonstrate the effectiveness of DeepCC.
A Fuzzy Expert System for Diagnosing and Analyzing Human Diseases

According to the World Health Organization (WHO), human disease results in at least 70% of deaths every year. Approximately, 56 million people died in 2012 and 68% of all deaths in 2012 were as a result of non-communicable diseases. The aim of this paper is to design and develop a web-based fuzzy expert system that would diagnose some of these diseases and provide users with expert advice and prescriptions based on the diagnosis generated by the system. The system would not only indicate if the disease is present but will also indicate the level at which the disease is present. The system is designed to diagnose five diseases which include asthma, diabetes, hypertension, malaria and tuberculosis. The system uses Mamdani inference method which has four phases: fuzzification, rule evaluation, rule aggregation and defuzzification. The fuzzy expert system was designed based on clinical observations and the expert knowledge. Having performed the experimentation and obtained relevant results, it is worthy of note that this approach of diagnosing human diseases has put the accuracy and reliability to 97%. It is the strong opinion of the authors that its full-scale implementation will assist in no small measure in carrying out same function in some of the hospitals and health institutions.
Eidos,

Rebecca Sharp, Adarsh Pyarelal, Benjamin Gyori, Keith Alcock, Egoitz Laparra, Marco A. Valenzuela-Escárcega, Ajay Nagesh, Vikas Yadav, John Bachman, Zheng Tang, Heather Lent, Fan Luo, Mithun Paul, Steven Bethard, Kobus Barnard, Clayton Morrison, Mihai Surdeanu. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). 2019.
Requirements Management Model (RMM): A Proposed Model for Successful Delivery of Software Projects

Requirements management is not easy task especially in global software development where clients and vendors are far away from each other's.For successful implementation of any software project, proper and efficient requirements management is necessary.Purpose of this current work is development of requirements management model (RMM) that can address the factors that have positive impact and the factors that have negative impact during management of requirements.The propose model will provide solutions to the challenges during requirement management.Systematic literature review (SLR) and empirical research study will be conducted for achieving the goals and objectives.The expected results of this study will be RMM that will help vendor organizations in better management of software requirements.
PintAR

In this demo, we present PintAR: an interactive prototyping tool that explores Augmented Reality for the design of interactive spatial experiences. Our system aims to remedy the lack of tools for rapid-prototyping situated experiences in AR without programming or 3D modeling. PintAR combines a digital paper and pen interface with a head-mounted display to allow users to sketch and interact with digital content in their environment. Users can take PintAR anywhere, leveraging objects and information available in the real world to bring context to their prototypes.
Normalizing Non-canonical Turkish Texts Using Machine Translation Approaches

With the growth of the social web, usergenerated text data has reached unprecedented sizes.Non-canonical text normalization provides a way to exploit this as a practical source of training data for language processing systems.The state of the art in Turkish text normalization is composed of a tokenlevel pipeline of modules, heavily dependent on external linguistic resources and manuallydefined rules.Instead, we propose a fullyautomated, context-aware machine translation approach with fewer stages of processing.Experiments with various implementations of our approach show that we are able to surpass the current best-performing system by a large margin.
Accurate and fast two-step phase shifting algorithm based on principle component analysis and Lissajous ellipse fitting with random phase shift and no pre-filtering

To achieve high measurement accuracy with less computational time-in-phase shifting interferometry, a random phase-shifting algorithm based on principal component analysis and Lissajous ellipse fitting (PCA&LEF) is proposed. It doesn't need pre-filtering and can obtain relatively accurate phase distribution with only two phase shifted interferograms and less computational time and is suitable for different background intensity, modulation amplitude distributions and noises. Moreover, it can obtain absolutely accurate result when the background intensity and modulation amplitude are perfect and can partly suppress the effect of imperfect background intensity and modulation amplitude. Last but not least, it removes the restriction that PCA needs more than three interferograms with well-distributed phase shifts to subtract relatively accurate mean. The simulations and experiments verify the correctness and feasibility of PCA&LEF.
Big

Shima Asaadi, Saif Mohammad, Svetlana Kiritchenko. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Cervical Cancer Risk Prediction Model and Analysis of Risk Factors based on Machine Learning

Cervical cancer, as one of the most common malignant tumor among women, is difficult to be diagnosed and studied due to its complexity of disease factors and challenged prediction. In this paper, a real data-driven powerful machine learning model is employed. With this technique, we model the detection methods of cervical cancer and determine the diagnostic accuracy of current mainstream methods for cervical cancer by multi-layer perceptron. Finally, the importance index of cervical cancer risk factors can be analyzed by random forest. The experiment results have shown that there is a close relationship between the risk factors and cervical cancer. And compared with other risk factors, age, number of sexual partners, hormonal contraceptives have a greater influence on the diagnosis of cervical cancer. Therefore, our research not only improves the predictability of cervical cancer risk, but also inspires the development of pathological model based on MLP and random forest.
Direct Acceleration of SAGA using Sampled Negative Momentum

Variance reduction is a simple and effective technique that accelerates convex (or non-convex) stochastic optimization. Among existing variance reduction methods, SVRG and SAGA adopt unbiased gradient estimators and are the most popular variance reduction methods in recent years. Although various accelerated variants of SVRG (e.g., Katyusha and Acc-Prox-SVRG) have been proposed, the direct acceleration of SAGA still remains unknown. In this paper, we propose a directly accelerated variant of SAGA using a novel Sampled Negative Momentum (SSNM), which achieves the best known oracle complexity for strongly convex problems (with known strong convexity parameter). Consequently, our work fills the void of directly accelerated SAGA.
Image Forensics Based on Lighting Estimation

Computer generated images are assumed to be a key part in each person’s life in this era of information technology, where individuals effectively inhabit the advertisements, magazines, websites, televisions and many more. At the point when digital images played their role, the event of violations in terms of misrepresentation of information, use of their wrong doings winds up and also becomes easier with the help of image editing application programs. To be legitimate, if anyone does wrong anything then the proposed method can be used for a correct identification of the forgery and the imitations in the digital images. In existing techniques, researchers have suggested most well-known types of digital photographic manipulations based on source, meta-data, image copying, splicing and many more. The proposed approach is inspired by physics-based techniques and requires less human involvement. The presented approach works for images having any type of objects present in the scene, i.e. not only limited to human faces and selection of same intensity regions of the image. By assessing the lighting parameters, the proposed technique identifies the manipulated object and returns angle of incidence w.r.t light source direction. The demonstrated result produces forgery recognition rate of 92% on an image dataset comprising of various types of manipulated images.
Sound and complete bidirectional typechecking for higher-rank polymorphism with existentials and indexed types

Bidirectional typechecking, in which terms either synthesize a type or are checked against a known type, has become popular for its applicability to a variety of type systems, its error reporting, and its ease of implementation. Following principles from proof theory, bidirectional typing can be applied to many type constructs. The principles underlying a bidirectional approach to indexed types (generalized algebraic datatypes) are less clear. Building on proof-theoretic treatments of equality, we give a declarative specification of typing based on focalization. This approach permits declarative rules for coverage of pattern matching, as well as support for first-class existential types using a focalized subtyping judgment. We use refinement types to avoid explicitly passing equality proofs in our term syntax, making our calculus similar to languages such as Haskell and OCaml. We also extend the declarative specification with an explicit rules for deducing when a type is principal, permitting us to give a complete declarative specification for a rich type system with significant type inference. We also give a set of algorithmic typing rules, and prove that it is sound and complete with respect to the declarative system. The proof requires a number of technical innovations, including proving soundness and completeness in a mutually recursive fashion.
An Improved Convolutional Network Architecture Based on Residual Modeling for Person Re-Identification in Edge Computing

Person re-identification is an important task in the field of video surveillance that concentrates on identifying the same person across different cameras. Some methods cannot learn effective image representations, due to the low resolution of pedestrian image data sets. In this article, we propose a novel Siamese network architecture with layers specially designed to address the problem of re-identification. The architecture proposed in this work is applied to the edge of the cloud infrastructure, which can accelerate the speed of pedestrian retrieval. Our network outputs a similarity value when a pair of images is given as input, indicating whether the two input images show the same person. Novel elements of our architecture include a residual model layer that includes an “identity block” and a “conv” block, which considerably capture more efficient features between the two input images. A global average pooling layer is adopted to reduce the model complexity before a fully connected layer, which minimizes person retrieval time in edge computing. Our proposed method significantly improves previous on: CUHK03 by 30% in rank-1, Market-1501 by 35% in rank-1. We also demonstrate that the proposed method outperforms most state-of-the-art methods on the two public benchmarks.
Low-Bit Quantization for Attributed Network Representation Learning

Attributed network embedding plays an important role in transferring network data into compact vectors for effective network analysis. Existing attributed network embedding models are designed either in continuous Euclidean spaces which introduce data redundancy or in binary coding spaces which incur significant loss of representation accuracy. To this end, we present a new Low-Bit Quantization for Attributed Network Representation Learning model (LQANR for short) that can learn compact node representations with low bitwidth values while preserving high representation accuracy. Specifically, we formulate a new representation learning function based on matrix factorization that can jointly learn the low-bit node representations and the layer aggregation weights under the low-bit quantization constraint. Because the new learning function falls into the category of mixed integer optimization, we propose an efficient mixed-integer based alternating direction method of multipliers (ADMM) algorithm as the solution. Experiments on real-world node classification and link prediction tasks validate the promising results of the proposed LQANR model.
A goal-driven software product line approach for evolving multi-agent systems in the Internet of Things

Multi-agent systems have proved to be a suitable technology for developing self-adaptive Internet of Things (IoT) systems, able to make the most appropriate decisions to address unexpected situations. This leads to new opportunities to use multi-agent technologies to develop all kinds of cyber–physical systems, which usually encompass a high diversity of devices (e.g., new home appliances). The heterogeneity of devices and the high diversity of the available technology, demand the explicit modeling of all kinds of variability for ultra-large systems. However, multi-agent systems lack mechanisms to effectively deal with the different degrees of variability present in these kinds of systems. Software Product Line (SPL) technologies, including variability models, have been successfully applied to different domains to explicitly model variability in hardware, system requirements or user-intended goals. In addition, current market trends are unpredictable, imposing novel technologies, new requirements and goals that must be incorporated immediately into the running systems without damaging them. In this paper, we combine goal-driven and SPL approaches to develop and drive the evolution of multi-agent systems in the context of cyber–physical systems. We propose an SPL process and an evolution process that define a set of models (iStar 2.0 for goals and CVL models for variability) and algorithms to automatically propagate changes to agents running in multiple heterogeneous devices, each of them with a different configuration. We illustrate the proposal in the context of a home energy management system. Finally, we have tested the scalability and performance of the proposal using randomly generated models. The results show that with our approach it is possible to manage huge iStar models of 10000 elements in seconds.
Bisecting commits and modeling commit risk during testing

Software testing is one of the costliest stages in the software development life cycle. One approach to reducing the test execution cost is to group changes and test them as a batch (i.e. batch testing). However, when tests fail in a batch, commits in the batch need to be re-tested to identify the cause of the failure, i.e. the culprit commit. The re-testing is typically done through bisection (i.e. a binary search through the commits in a batch). Intuitively, the effectiveness of batch testing highly depends on the size of the batch. Larger batches require fewer initial test runs, but have a higher chance of a test failure that can lead to expensive test re-runs to find the culprit. We are unaware of research that investigates and simulates the impact of batch sizes on the cost of testing in industry. In this work, we first conduct empirical studies on the effectiveness of batch testing in three large-scale industrial software systems at Ericsson. Using 9 months of testing data, we simulate batch sizes from 1 to 20 and find the most cost-effective BatchSize for each project. Our results show that batch testing saves 72% of test executions compared to testing each commit individually. In a second simulation, we incorporate flaky tests that pass and fail on the same commit as they are a significant source of additional test executions on large projects. We model the degree of flakiness for each project and find that test flakiness reduces the cost savings to 42%. In a third simulation, we guide bisection to reduce the likelihood of batch-testing failures. We model the riskiness of each commit in a batch using a bug model and a test execution history model. The risky commits are tested individually, while the less risky commits are tested in a single larger batch. Culprit predictions with our approach reduce test executions up to 9% compared to Ericsson’s current bisection approach. The results have been adopted by developers at Ericsson and a tool to guide bisection is in the process of being added to Ericsson’s continuous integration pipeline.