Reviewerly/exHarmony huggingface dataset:
Blind Detection of Communication Signals Based on Improved YOLO3

Blind detection of communication signals is a challenging task. In this paper, a general and novel blind detection method is proposed based on the similarity between communication signal detection and image object detection. We designed an improved YOLO3 model to detect the communication signals contained in the 2D wide-band spectrograms, the main innovates are as follows: 1) in order to reduce the burden of spectrograms labeling, an ingenious and automatic signal object labeling method is proposed; 2) in view of the fact that the communication signals are long and narrow objects in the spectrograms, the corresponding prior anchors are designed to improve the detection probability; 3) in order to improve the training efficiency and detection accuracy, the CIOU cost function and DIOU-NMS inference algorithm are introduced to achieve high-precision signal detection. The simulation results demonstrate that the proposed method can effectively detect the continuous and burst signals in wide-band communication signal data, and its performance is better than the traditional energy detection method.
Denoising, Outlier/Dropout Correction, and Sensor Selection in Range-Based Positioning

Range-based target localization combines the range measurements and appropriate algorithms [such as time of arrival (TOA)] to determine the real-time position of a moving target not only in a satellite-denied environment but also for high-precision applications in an open (i.e., satellite-available) environment. However, the measurements from sensors always suffer from uncertainties, such as noises, outliers, dropouts, and biases, which make measurements not reliable enough to directly utilize in positioning. This article is, therefore, concerned with two kinds of sensor correction problems: 1) attenuating the noises, removing the outliers, and completing the dropouts and 2) identifying the discredited sensors and forbidding them from use. Specifically, we model a range measurement time series from a ranging sensor as a nonstationary stochastic process and then use a local polynomial to regress the mean function in an online manner. The derivatives of the mean function are identified as the state variables in the state space, and the Kalman filter is used to estimate the states. We will show that: 1) the proposed method is simple yet effective in denoising the measurements and correcting the outliers/dropouts with very high accuracy and precision and 2) the defined and estimated states can clearly reflect the pattern of one type of nonspecific anomaly contained in the range time series, which enables the recognition of the dysfunctional sensors.
Towards Practical Applications in Modeling Blockchain System

Like multiservice networks, blockchain technology is currently experiencing significant development because of its decentralization and ability to organize secure, seamless, reliable data exchange and storage. Due to the significant demand for the technology, there is a need to analyze the impact of these technology processes on network characteristics to predict traffic behavior and ensure required quality indicators, as well as on the stability of public communication network elements when blockchain technology operates. Conducting a full-scale experiment is a time-consuming task that cannot always be accomplished, so in this paper, the authors propose considering approaches to modeling these systems and, as an example, propose to use a simulation system to assess the performance of the network and its elements.
Multi-Tier Federated Learning for Vertically Partitioned Data

We consider decentralized model training in tiered communication networks. Our network model consists of a set of silos, each holding a vertical partition of the data. Each silo contains a hub and a set of clients, with the silo’s vertical data shard partitioned horizontally across its clients. We propose Tiered Decentralized Coordinate Descent (TDCD), a communication-efficient decentralized training algorithm for such two-tiered networks. To reduce communication overhead, the clients in each silo perform multiple local gradient steps before sharing updates with their hub. Each hub adjusts its coordinates by averaging its workers’ updates, and then hubs exchange intermediate updates with one another. We present a theoretical analysis of our algorithm and show the dependence of the convergence rate on the number of vertical partitions, the number of local updates, and the number of clients in each hub. We further validate our approach empirically via simulation-based experiments using a variety of datasets and both convex and non-convex objectives.
Graphspeech: Syntax-Aware Graph Attention Network for Neural Speech Synthesis

Attention-based end-to-end text-to-speech synthesis (TTS) is superior to conventional statistical methods in many ways. Transformer-based TTS is one of such successful implementations. While Transformer TTS models the speech frame sequence well with a self-attention mechanism, it does not associate input text with output utterances from a syntactic point of view at sentence level. We propose a novel neural TTS model, denoted as GraphSpeech, that is formulated under graph neural network framework. GraphSpeech encodes explicitly the syntactic relation of input lexical tokens in a sentence, and incorporates such information to derive syntactically motivated character embeddings for TTS attention mechanism. Experiments show that GraphSpeech consistently outperforms the Transformer TTS baseline in terms of spectrum and prosody rendering of utterances.
Recording, Visualising and Understanding Developer Programming Behaviour

To understand how developers solve programming tasks, it is necessary to observe what they are doing, i.e., what specific actions they perform, which strategies they apply and how they make use of possibly present, but not yet known or otherwise not yet available information that is needed to solve a task. To do so, we implemented a plug-in for the Eclipse IDE which captures nearly every interaction with the IDE a developer performs when working on a programming task. This enables us to comprehensively track a developer&#x2019;s behaviour, e.g., whether and when a code edit needed for solving a given task was performed, and even more interesting, what were the preceding steps that led the developer to do so. In a first experiment conducted with the new plug-in, we were able to observe action patterns and program comprehension stages that confirm results of previous studies as well as were partly only suspected until now by recent literature, but never truly observed before.Screencast of the tool: https://youtu.be/GeZI-vCdgfoClosed caption version: https://youtu.be/mgt6Q-t7U00
A Detection-based Approach to Multiview Action Classification in Infants

Activity recognition in children and infants is important in applications such as safety monitoring, behavior assessment, and child-robot interaction, among others. However, it differs from activity recognition in adults not only because body poses and proportions are different, but also because of the way in which actions are performed. This paper addresses the problem of infant action classification in challenging conditions. The actions are performed in a pediatric rehabilitation environment in which not only infants but also robots and adults are present, with the infant being one of the smallest actors in the scene. We propose a multiview action classification system based on Faster R-CNN and LSTM networks, which fuses information from different views by using learnable fusion coefficients derived from detection confidence scores. The proposed system is view-independent, learns features that are close to view-invariant, and can handle new or missing views at test time. Our approach outperforms the state-of-the-art baseline model for a small dataset (2 subjects, 10-24 months old) by 11.4% in terms of average classification accuracy in four classes (crawl, sit, stand and walk). Moreover, experiments in an extended dataset (6 subjects, 8-24 months old) show that the proposed fusion strategy outperforms all the alternative fusion methods studied.
Latent print quality in blind proficiency testing: Using quality metrics to examine laboratory performance

Calls for blind proficiency testing in forensic science disciplines intensified following the 2009 National Academy of Sciences report and were echoed in the 2016 report by the President's Council of Advisors on Science and Technology. Both practitioners and scholars have noted that "open" proficiency tests, in which analysts know they are being tested, allow for test-taking behavior that is not representative of behavior in routine casework. This study reports the outcomes of one laboratory's blind quality control (BQC) program. Specifically, we describe results from approximately 2.5 years of blind cases in the latent print section (N = 376 latent prints submitted as part of 144 cases). We also used a widely available quality metrics software (LQMetrics) to explore relationships between objective print quality and case outcomes. Results revealed that nearly all BQC prints (92.0%) were of sufficient quality to enter into AFIS. When prints had a source present in AFIS, 41.7% of print searches resulted in a candidate list containing the true source. Examiners committed no false positive errors but other types of errors were more common. Average print quality was in the midpoint of the range (53.4 on a 0-to-100 scale), though prints were evenly distributed across the Good, Bad, and Ugly categories. Quality metrics were significantly associated with sufficiency determinations, examiner conclusions, and examiner accuracy. Implications for blind testing and the use of quality metrics in routine casework as well as proficiency testing are discussed.
Sentiment Injected Iteratively Co-Interactive Network for Spoken Language Understanding

Spoken Language Understanding (SLU) is an essential part of the spoken dialogue system, which typically consists of intent detection (ID) and slot filling (SF) tasks. During the conversation, most utterances of people contain rich sentimental information, which is helpful for performing the ID and SF tasks but ignored to be explored by existing works. In this paper, we argue that implicitly introducing sentimental features can promote SLU performance. Specifically, we present a Multitask Learning (MTL) framework to implicitly extract and utilize the aspect-based sentimental text features. Besides, we introduce an Iteratively Co-Interactive Network (ICN) for the SLU task to fully utilize the comprehensive text features. Experimental results show that with the external BERT representation, our framework achieves new state-of-the-art on two benchmark datasets, i.e., SNIPS and ATIS.
Early Life Cycle Software Defect Prediction. Why? How?

Many researchers assume that, for software analytics, "more data is better." We write to show that, at least for learning defect predictors, this may not be true. To demonstrate this, we analyzed hundreds of popular GitHub projects. These projects ran for 84 months and contained 3,728 commits (median values). Across these projects, most of the defects occur very early in their life cycle. Hence, defect predictors learned from the first 150 commits and four months perform just as well as anything else. This means that, at least for the projects studied here, after the first few months, we need not continually update our defect prediction models. We hope these results inspire other researchers to adopt a "simplicity-first" approach to their work. Some domains require a complex and data-hungry analysis. But before assuming complexity, it is prudent to check the raw data looking for "short cuts" that can simplify the analysis.
Context-based Image Caption using Deep Learning

Image captioning is an important but difficult task. The existing image caption mainly adopts the encoding and decoding structure, the encoder mainly uses CNN as image feature extractors, and the decoder uses LSTM. The attention mechanism is also widely used in the current encoding and decoding structure. However, the existing image caption models based on the convolutional neural networks and recurrent neural networks have low accuracy in extracting useful information from images and have problems such as gradient explosion. To solve these problems, this paper proposes a context-based image caption generation model. The method applies Resnet and context-coding for feature extraction SCST, then SCST and LSTM is used for captioning The experimental results demonstrates the effectiveness of the proposed approach.
Self-paced resistance learning against overfitting on noisy labels

• We propose a novel resistance loss to alleviate model overfitting • We propose a novel framework to robustly train CNNs on noisy labels • It integrates CNNs’ memorization effect, curriculum learning with a resis-tance loss Noisy labels composed of correct and corrupted ones are pervasive in practice. They might significantly deteriorate the performance of convolutional neural networks (CNNs), because CNNs are easily overfitted on corrupted labels. To address this issue, inspired by an observation, deep neural networks might first memorize the probably correct-label data and then corrupt-label samples, we propose a novel yet simple self-paced resistance framework to resist corrupted labels, without using any clean validation data. The proposed framework first utilizes the memorization effect of CNNs to learn a curriculum, which contains confident samples and provides meaningful supervision for other training samples. Then it adopts selected confident samples and a proposed resistance loss to update model parameters; the resistance loss tends to smooth model parameters’ update or attain equivalent prediction over each class, thereby resisting model overfitting on corrupted labels. Finally, we unify these two modules into a single loss function and optimize it in an alternative learning. Extensive experiments demonstrate the significantly superior performance of the proposed framework over recent state-of-the-art methods on noisy-label data. Source codes of the proposed method are available on https://github.com/xsshi2015/Self-paced-Resistance-Learning .
PERMARUN- A Persuasive Game to Improve User Awareness and Self-Efficacy Towards Secure Smartphone Behaviour

Android smartphones have undergone various changes and, uniformity in design (both hardware and software) has become common in most phones. Yet, security and privacy issues persist and grow along with the evolution of smartphones. Recent research shows that user awareness is still low, and they do not follow secure behaviour while using smartphones. Although a handful of works exist for improving user awareness and self-efficacy, none of them educate the users about Android Permissions in a contextual manner. In this paper, we discuss our persuasive game - "PermaRun", which teaches and motivates users to follow secure smartphone behaviour, increases user awareness and self-efficacy about android permissions. We conducted a Heuristic Evaluation for Playability (HEP) and Persuasiveness Evaluation (PE). The result shows that players had a positive experience playing the game, and they found the game playable and persuasive.
Discriminative information preservation: A general framework for unsupervised visual Domain Adaptation

Transfer learning (TL) has been proposed as an effective way to learn the classifier of the new domain using the rich supervision information available in a variety of other related domains. For successful transfer of information from related domains to the new domain, existing TL methods attempt to minimize both the geometric shift and distribution shift of the domains, and preserve the source domain discriminant information and the original similarity of the data. However, these methods fail to preserve target domain discriminative information due to the unavailability of target domain labeled data. Furthermore, they consider initial space data that may be unnecessary or irrelevant to the final classification. To this end, a novel Discriminative Information Preservation: a general framework for unsupervised visual Domain Adaptation (DIPDA) is proposed in this paper. Specifically, DIPDA considers low dimensional manifolds and preserves the target domain discriminative information (i.e., maximizing between-class variance and minimizing within-class variance) along with other important objectives. As a result, the most informative features can be extracted and the complexity of the model is significantly reduced. Moreover, the proposed DIPDA method has been extended to nonlinear problems in a Reproducing Kernel Hilbert Space (RKHS) using linear and RBF kernel functions. Compared with several state-of-the-art primitive, shallow and deep domain adaptation methods, DIPDA substantially improves the classification results on four widely used real-world domain adaptation datasets, which verifies the effectiveness and efficiency of the proposed methods. • A novel feature learning method DIPDA is proposed for unsupervised domain adaptation. • DIPDA is extended to kernelized version with linear and RBF kernel functions. • manifold subspaces are considered to reduce time complexity and to improve accuracy. • Both domain discriminative information is preserved to obtain well separated clusters. • The validity of the proposed methods is verified by extensive comparison experiments.
DeepCon: Contribution Coverage Testing for Deep Learning Systems

Deep learning (DL) has been widely adopted in many safety-critical scenarios. Deep neural networks (DNNs) usually play the core part in these DL systems. Existing studies have shown that DNNs can suffer from various vulnerabilities, and cause severe consequences. To improve the testing adequacy of DNNs, researchers have proposed several coverage criteria, e.g., neuron coverage in DeepXplore. The prediction result of a DNN is jointly determined by the outputs of neurons and the connection weights that they connect into next-level neurons. However, existing coverage criteria use only the output of a neuron to determine the activation state of the neuron and ignore the connection weights it emits.In this paper, we propose DeepCon, a novel contribution coverage. In DeepCon, we define a term contribution as the combination of the output of a neuron and the connection weight it emits, and use the contribution coverage to gauge the testing adequacy of DNNs. DeepCon can thoroughly cover both neurons and the connection weights they emit and can scale well to large DNNs. We further propose a contribution coverage guided test generation approach, DeepCon-Gen, which can automatically generate tests and activate inactivated contributions of DNNs. We evaluate DeepCon and DeepCon-Gen on five different DNNs over two popular datasets. The experimental results show that DeepCon can well present the testing adequacy of these DNNs. DeepCon-Gen can effectively activate the inactivated contributions, and 62.6% of the generated tests can lead to mispredictions.
Privacy-preserving logistic regression with secret sharing

Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Researchers that collect and combine datasets from various data custodians and jurisdictions can greatly benefit from the increased statistical power to support their analysis goals. However, combining data from different sources creates serious privacy concerns that need to be addressed.In this paper, we propose two privacy-preserving protocols for performing logistic regression with the Newton-Raphson method in the estimation of parameters. Our proposals are based on secure Multi-Party Computation (MPC) and tailored to the honest majority and dishonest majority security settings.The proposed protocols are evaluated against both synthetic and real-world datasets in terms of efficiency and accuracy, and a comparison is made with the ordinary logistic regression. The experimental results demonstrate that the proposed protocols are highly efficient and accurate.Our work introduces two iterative algorithms to enable the distributed training of a logistic regression model in a privacy-preserving manner. The implementation results show that our algorithms can handle large datasets from multiple sources.
Collaborative VR-Based 3D Labeling of Live-Captured Scenes by Remote Users

Previous work on interactive 3D labeling focused on improving user experience based on virtual/augmented reality and, thereby, speeding-up the labeling of scenes. In this article, we present a novel interactive, collaborative VR-based 3D labeling system for live-captured scenes by multiple remotely connected users based on sparse multi-user input with automatic label propagation and completion. Hence, our system is particularly beneficial in the case of multiple users that are able to label different scene parts from the respectively adequate views in parallel. Our proposed system relies on 1) the RGB-D capture of an environment by a user, 2) a reconstruction client that integrates this stream into a 3D model, 3) a server that gets scene updates and manages the global 3D scene model as well as client requests and the integration/propagation of labels, 4) labeling clients that allow an independent VR-based scene exploration and labeling for each user, and 5) remotely connected users that provide a sparse 3D labeling used to control the label propagation over objects and the label prediction to other scene parts. Our evaluation demonstrates the intuitive collaborative 3D labeling experience as well as its capability to meet the efficiency constraints regarding reconstruction speed, data streaming, visualization, and labeling.
Adversarial Generative Distance-Based Classifier for Robust Out-of-Domain Detection

Detecting out-of-domain (OOD) intents is critical in a task-oriented dialog system. Existing methods rely heavily on extensive manually labeled OOD samples and lack robustness. In this paper, we propose an efficient adversarial attack mechanism to augment hard OOD samples and design a novel generative distance-based classifier to detect OOD samples instead of a traditional threshold-based discriminator classifier. Experiments on two public benchmark datasets show that our method can consistently outperform the baselines with a statistically significant margin.
Clustering of Research Documents - A Survey on Semantic Analysis and Keyword Extraction

Natural Language Processing (NLP) has always been a major focus of companies and institutions alike. There have been many significant contributions in this field of Computer Science. As every year publications are contributed in the different research domain, there is a need for keyword extraction and document clustering. This helps in organizing the publications of an Institution. The paper aims at giving an overview of various keyword extraction and document clustering algorithms. This study contributes to the area of text and document-based search engines. Also, the study helps to identify the domain of research documents and to categorize them based on their semantics. The paper aims in giving a comparative study of different keyword extraction algorithm and clustering algorithm by which one can model a new prototype for document-based search engines and categorizing the documents based on various mainstream research domains of Computer Science.