Reviewerly/exHarmony huggingface dataset:
Gaussian Filter for Nonlinear Networked Systems With Synchronously Correlated Noises and One- Step Randomly Delayed Measurements and Multiple Packet Dropouts

In this paper, a Gaussian recursive filter is proposed for nonlinear networked systems with synchronously correlated noises and one-step randomly delayed measurements and multiple packet dropouts. Since each packet at the sensor side is sent only once and the system has one-step delay and multiple packet dropouts, the data processing center may receive zero/one/two measurements. By augmenting the system state at last epoch to the current one, employing two Bernoulli distributed random variables to describe the phenomena of delay and packet dropouts and using the prediction compensation mechanism, a novel model is derived. At the same time, to deal with the problem of correlated noise, Gaussian approximation recursive filter (GASF) is used. Based on the above processing, a nonlinear Gaussian recursive filter and corresponding numerical implementation based on cubature Kalman filter (CKF) are given. Compared with the existing results in linear systems, the proposed algorithm is more general and has higher estimation accuracy in nonlinear systems. The simulation results show the effectiveness of the proposed algorithm.
Energy Efficiency Enhancement for CNN-based Deep Mobile Sensing

Recently, deep learning has been used to tackle mobile sensing problems, and the inference phase of deep learning is preferred to be run on mobile devices for speedy responses. However, mobile devices are resource-constrained platforms for both computation and power. Moreover, an inference task with deep learning involves tens of billions of mathematical operations and tens of millions of parameter reads. Thus, it is a critical issue to reduce the energy consumption of deep learning inference algorithms. In this article, we survey various energy reduction approaches, and classify them into three categories: the compressing neural network model, minimizing the data transfer required in computation, and offloading workloads. Moreover, we simulate and compare three techniques of model compression, by applying them to an object recognition problem.
Toward efficient gradual typing for structural types via coercions

Gradual typing combines static and dynamic typing in the same program. Siek et al. (2015) describe five criteria for gradually typed languages, including type soundness and the gradual guarantee. A significant number of languages have been developed in academia and industry that support some of these criteria (TypeScript, Typed Racket, Safe TypeScript, Transient Reticulated Python, Thorn, etc.) but relatively few support all the criteria (Nom, Gradualtalk, Guarded Reticulated Python). Of those that do, only Nom does so efficiently. The Nom experiment shows that one can achieve efficient gradual typing in languages with only nominal types, but many languages have structural types: function types, tuples, record and object types, generics, etc.
Evaluating and empirically improving the visual syntax of use case diagrams

Use case modeling is a forefront technique to specify functional requirements of a system. Many research works related to use case modeling have been devoted to improving various aspects of use case modeling and its utilization in software development processes. One key aspect of use case models that has thus far been overlooked by the research community is the visual perception of use case diagrams by its readers. Any model is used transfer a mental idea by a modeler to a model reader. Even if a use case diagram is constructed flawlessly, if it is misread or misinterpreted by its reader then the intrinsic purpose of modeling has failed. This paper provides a two-fold contribution. Firstly, this paper presents an evaluation of the cognitive effectiveness of use case diagrams notation. The evaluation is based on theory principles and empirical evidence mainly from the cognitive science field. Secondly, it provides empirically validated improvements to the use case diagram notation that enhances its cognitive effectiveness. Empirical validation of the improvements is drawn by conducting an industrial survey using business analyst professionals. Empirical validation is also drawn by conducting an experiment using software engineering professionals as subjects.
Fast &amp; Strong

String dictionaries constitute a large portion of the memory foot-print of database applications. While strong string dictionary compression algorithms exist, these come with impractical access and compression times. Therefore, lightweight algorithms such as front coding are favored in practice. This paper endeavors to make strong string dictionary compression practical. We focus on Re-Pair Front Coding (RPFC), a grammar-based compression algorithm, since it consistently offers better compression ratios than other algorithms in the literature. To accelerate compression times, we propose block-based RPFC, which consists in compressing independently small blocks of the dictionary. Moreover, to accelerate access times, we devise a vectorized access method, using Intel® Advanced Vector Extensions 512 (Intel® AVX-512), that is enabled by two specific changes we propose to RPFC. Our experimental evaluation shows that our proposed techniques accelerate compression and access times by up to 24x and 2.9x, respectively. These results move our modified RPFC into a practical range for use in database systems.
Auto-CVE

Automated Machine Learning (Auto-ML) is a growing field receiving a lot of attention. Several techniques are being developed to address the question of how to automate the process of defining machine learning pipelines, using diverse types of approaches and with relative success, but still this problem is far from being solved.
Neural trees with peer-to-peer and server-to-client knowledge transferring models for high-dimensional data classification

Classification of the high-dimensional data by a new expert system is followed in the current paper. The proposed system defines some non-disjoint clusters of highly relevant features with the least inner-redundancy. For each cluster, a neural tree is implemented exploiting an Extreme Learning Machine (ELM) together an inference engine in any node. The derived classification rules from ELM are stored in the rule-base of the inference engine to recognize the classes. A majority voting is used to unify the results of the different neural trees. This structure is refereed as the Forest of Extreme Learning Machines with Rule-base Transferring (FELM-RT). The contribution of FELM-RT is to decrease the duplicated computations by using two novel interaction models between the neural trees. In the first interaction model, namely Peer-to-Peer (P2P) model, each node can share its rule-base with the other nodes of the various neural trees. In the second that is referred as Server-to-Client (S2C) model, a neural tree that works on a cluster with the best relevancy and redundancy, shares the rules with the other neural trees. In both of the models, a fuzzy aggregation technique is used to adjust the certainty of the rules. The processing time of FELM-RT decreases essentially and it improves the classification accuracy. The high results of F-measure and G-mean, show that FELM-RT classifies the high-dimensional datasets without over-fitting. The comparison between FELM-RT and some state-of-the-art classifiers reveals that FELM-RT overcomes them specially on the datasets with more than 3 million features.
Can Issues Reported at Stack Overflow Questions be Reproduced? An Exploratory Study

Software developers often look for solutions to their code level problems at Stack Overflow. Hence, they frequently submit their questions with sample code segments and issue descriptions. Unfortunately, it is not always possible to reproduce their reported issues from such code segments. This phenomenon might prevent their questions from getting prompt and appropriate solutions. In this paper, we report an exploratory study on the reproducibility of the issues discussed in 400 questions of Stack Overflow. In particular, we parse, compile, execute and even carefully examine the code segments from these questions, spent a total of 200 man hours, and then attempt to reproduce their programming issues. The outcomes of our study are two-fold. First, we find that 68% of the code segments require minor and major modifications in order to reproduce the issues reported by the developers. On the contrary, 22% code segments completely fail to reproduce the issues. We also carefully investigate why these issues could not be reproduced and then provide evidence-based guidelines for writing effective code examples for Stack Overflow questions. Second, we investigate the correlation between issue reproducibility status (of questions) and corresponding answer meta-data such as the presence of an accepted answer. According to our analysis, a question with reproducible issues has at least three times higher chance of receiving an accepted answer than the question with irreproducible issues.
Safe Velocity: A Practical Guide to Software Deployment at Scale using Controlled Rollout

Software companies are increasingly adopting novel approaches to ensure their products perform correctly, succeed in improving user experience and for increasing revenue. Two approaches that have significantly impacted product development are controlled experiments - concurrent experiments with different variations of the same product, and phased rollouts - deployments to smaller audiences (rings) before deploying broadly. Although powerful in isolation, product teams experience most benefits when the two approaches are integrated. Intuitively, combining them may seem trivial. However, in practice and at a large scale, this is difficult. For example, it requires careful data analysis to correctly handle exposed populations, determine the duration of exposure, and identify the differences between the populations. All of these are needed to optimize the likelihood of successful deployments, maximize learnings, and minimize potential harm to users of the products. In this paper, based on case study research at Microsoft, we introduce controlled rollout (CRL), which applies controlled experimentation to each ring of a traditional phased rollout. We describe its implementation on several products used by hundreds of millions of users along with the complexities encountered and overcome. In particular, we explain strategies for selecting the length of the rollout period and metrics of focus, and defining the pass criterion for each of the rings. Finally, we evaluate the effectiveness of CRL by examining hundreds of controlled rollouts at Microsoft Office. With our work, we hope to help other companies in optimizing their software deployment practices.
Identifying incidental findings from radiology reports of trauma patients: An evaluation of automated feature representation methods

Radiologic imaging of trauma patients often uncovers findings that are unrelated to the trauma. These are termed as incidental findings and identifying them in radiology examination reports is necessary for appropriate follow-up. We developed and evaluated an automated pipeline to identify incidental findings at sentence and section levels in radiology reports of trauma patients.We created an annotated dataset of 4,181 reports and investigated automated feature representations including traditional word and clinical concept (such as SNOMED CT) representations, as well as word and concept embeddings. We evaluated these representations by using them with traditional classifiers such as logistic regression and with deep learning methods such as convolutional neural networks (CNNs).The best performance was observed using word embeddings with CNNs with F1 scores of 0.66 and 0.52 at section and sentence levels respectively. The F1 score was statistically significantly higher for sections compared to sentences (Wilcoxon; Z < 0.001, p < 0.05). Compared to using words alone, the addition of SNOMED CT concepts did not improve performance. At the sentence level, the F1 score improved significantly from 0.46 to 0.52 when using pre-trained embeddings (Wilcoxon; Z < 0.001, p < 0.05).The results show that the best performance was achieved by using embeddings with CNNs at both sentence and section levels. This provides evidence that such a pipeline is capable of accurately identifying incidental findings in radiology reports in an automated manner.
Detecting Pickpocketing Gangs on Buses with Smart Card Data

Detecting pickpocketing gangs on buses is critical for safety and public security department. Knowing this both in real time and from historical records would allow effective law enforcement and crime prevention. However, very little research has been devoted into identifying pickpocketing gangs in an automated and holistic manner. This research utilizes smart card data generated by bus riders to identify pickpocketing gangs, who possess distinct characteristics from regular passengers. Particularly, we create a dataset of 1,098 pickpockets among 4.06 million bus riders in August, 2015 in Beijing automatically and efficiently based on an efficient labeling model of outliers. This model examines anomaly of passengers using the so-called relative outlier cluster factor and local outlier factor. The proposed mobility patterns of pickpockets are then learned based on supervised classification. Pickpockets from the derived dataset form a pickpocketing network, which is modeled as a graph with vertices denoted as discrete pickpockets, and edge weight quantified by a combined similarity on mobility pattern, space and time. A graphbased Louvain algorithm is adopted to detect pickpocketing gangs. Experiments are conducted on SINA microblog data to verify the detected pickpocketing gangs identified by the proposed framework. Results show that the framework detects 63 pickpocketing gangs and verifies 34 gangs by microblogs, with recall value 0.85. Findings from this research can assist police and public safety departments in the city in taking pro-active actions to track down pickpocketing gangs.
Meta Classifier-Based Ensemble Learning For Sentiment Classification

In the era of digital explosion, a huge amount of data is generated every second from different sources, which requires rigorous analysis for decision-making and knowledge gathering. Recently, one such challenging issue that found to be interesting to many researchers is sentiment classification through opinion mining. From the latest research on sentiment analysis, it has been also observed that ensemble-based supervised machine learning algorithms outperform individual classifiers. Ensemble methods are meta algorithms that combine several machine learning algorithms into one predictive model, and thereby decreases variance and bias and improve classifiers prediction capability. In this paper, we have evaluated the performance of ensemble methods in conjunction with meta algorithm on two different datasets. Majority voting algorithm has also been employed for prediction decision of sentiment classification for our model. The experimental result shows that meta classifier-based ensemble approach outperform individual classifier in some cases.
Robust Adaptive Graph Regularized Non-Negative Matrix Factorization

Data clustering, which aims to divide the given samples into several different groups, has drawn much attention in recent years.As a powerful tool, non-negative matrix factorization (NMF) has been applied successfully in clustering tasks.However, there are still two main limitations.First, the original NMF treats equally both noisy and clean data, which leads to high sensitivity to noises and outliers.Second, the performance of graph-based NMFs highly depends on the input graph, that is, if a low-quality graph is constructed to regularize NMF, the clustering results will be bad.To address the above-mentioned problems, we propose a novel robust adaptive graph regularized non-negative matrix factorization (RAGNMF) for data clustering.To be specific, we develop a robust weighted NMF (RWNMF) that can assign small weights to noises and outliers and large weights to clean data.Thus, the robustness of NMF is improved.Moreover, in the process of matrix factorization, metric learning is combined to choose some discriminative features and compute more appropriate distances of samples.Then, an adaptive graph is learned to well regularize the NMF.The experimental results demonstrate that the proposed RAGNMF can achieve better clustering performance then most of the state-of-the-art methods.
The formulaicity of translations across EU institutional genres

Abstract This paper explores the formulaicity of EU translations into Polish across four institutional genres (legislation, judgments, reports, websites) with reference to the corresponding EU English corpora in order to understand how the degree of formulaicity is affected by the variable of genre. Formulaicity is operationalised as lexical bundles – high-frequency multi-word sequences ( Biber and Barbieri 2007 ). The study shows a strong correlation between formulaicity and genres, as well as multiple facets of formulaicity (e.g. tokens vs. types). Our findings generally confirm the increased aggregate formulaicity of translations as regards bundle tokens for all EU genres, except for judgments, and the increased variation of bundles (types) for all the genres at the 40 occurrences per million words (pmw) threshold, although the “micro-formulaicity” threshold yielded inconclusive results. Another finding reveals a consistently low overlap of bundles between translations and non-translations. We argue that translations develop their own formulaic profiles which are levelled out compared to EU English corpora and which minimally overlap with formulaic profiles of domestic genres.
The Need for Alignment in Rate-Efficient Distributed Two-Sided Secure Matrix Computation

Computationally efficient matrix multiplication is a fundamental requirement in various fields, including and particularly in data analytics. To do so, the computation task of a large-scale matrix multiplication is typically outsourced to multiple servers. However, due to data misusage at the servers, security is typically of concern. In this paper, we study the two-sided secure matrix multiplication problem, where a user is interested in the matrix product AB of two finite field private matrices A and B. In this problem, the user exploits the computational resources of N servers to compute the matrix product, but simultaneously tries to conceal the private matrices from the servers. Our goal is to maximize the communication rate while preserving security, where we allow for up to ℓ ≤ N servers to collude. To this end, we propose a general aligned secret sharing and matrix partition scheme for which we optimize the partition of matrices A and B as a function of N and ℓ in order to maximize the achievable rate. A proposed inductive approach gives us an analytical close-to-optimal solution which significantly outperforms the existing scheme of Chang and Tandon in terms of (i) communication rate, (ii) maximum tolerable number of colluding servers and (iii) computational complexity.
Transfer Learning from Nucleus Detection to Classification in Histopathology Images

Despite significant recent success, modern computer vision techniques such as Convolutional Neural Networks (CNNs) are expensive to apply to cell-level prediction problems in histopathology images due to difficulties in providing celllevel supervision. This work explores the transferability of features learned by an object detection CNN (Faster R-CNN) to nucleus classification in histopathology images. We detect nuclei in these images using class-agnostic models trained on small annotated patches, and use the CNN representations of detected nuclei to cluster and classify them. We show that with a small training dataset, the proposed pipeline can achieve superior nucleus classification performance, and generalizes well to unseen stain types.
Dual CNN for Relation Extraction with Knowledge-Based Attention and Word Embeddings

Relation extraction is the underlying critical task of textual understanding. However, the existing methods currently have defects in instance selection and lack background knowledge for entity recognition. In this paper, we propose a knowledge-based attention model, which can make full use of supervised information from a knowledge base, to select an entity. We also design a method of dual convolutional neural networks (CNNs) considering the word embedding of each word is restricted by using a single training tool. The proposed model combines a CNN with an attention mechanism. The model inserts the word embedding and supervised information from the knowledge base into the CNN, performs convolution and pooling, and combines the knowledge base and CNN in the full connection layer. Based on these processes, the model not only obtains better entity representations but also improves the performance of relation extraction with the help of rich background knowledge. The experimental results demonstrate that the proposed model achieves competitive performance.
Information Flow Control for Distributed Trusted Execution Environments

Distributed applications cannot assume that their security policies will be enforced on untrusted hosts.Trusted execution environments (TEEs) combined with cryptographic mechanisms enable execution of known code on an untrusted host and the exchange of confidential and authenticated messages with it.TEEs do not, however, establish the trustworthiness of code executing in a TEE.Thus, developing secure applications using TEEs requires specialized expertise and careful auditing.This paper presents DFLATE, a core security calculus for distributed applications with TEEs.DFLATE offers high-level abstractions that reflect both the guarantees and limitations of the underlying security mechanisms they are based on.The accuracy of these abstractions is exhibited by asymmetry between confidentiality and integrity in our formal results: DFLATE enforces a strong form of noninterference for confidentiality, but only a weak form for integrity.This reflects the asymmetry of the security guarantees of a TEE: a malicious host cannot access secrets in the TEE or modify its contents, but they can suppress or manipulate the sequence of its inputs and outputs.Therefore DFLATE cannot protect against the suppression of high-integrity messages, but when these messages are delivered, their contents cannot have been influenced by an attacker.