Reviewerly/exHarmony huggingface dataset:
Tree Echo State Autoencoders with Grammars

Tree data occurs in many forms, such as computer programs, chemical molecules, or natural language. Unfortunately, the non-vectorial and discrete nature of trees makes it challenging to construct functions with tree-formed output, complicating tasks such as optimization or time series prediction. Autoencoders address this challenge by mapping trees to a vectorial latent space, where tasks are easier to solve, and then mapping the solution back to a tree structure. However, existing autoencoding approaches for tree data fail to take the specific grammatical structure of tree domains into account and rely on deep learning, thus requiring large training datasets and long training times. In this paper, we propose tree echo state autoencoders (TES-AE), which are guided by a tree grammar and can be trained within seconds by virtue of reservoir computing. In our evaluation on three datasets, we demonstrate that our proposed approach is not only much faster than a state-of-the-art deep learning autoencoding approach (D-VAE) but also has less autoencoding error if little data and time is given.
Variability aware requirements reuse analysis

Problem: The goal of a software product line is to aid quick and quality delivery of software products, sharing common features. Effectively achieving the above-mentioned goals requires reuse analysis of the product line features. Existing requirements reuse analysis approaches are not focused on recommending product line features, that can be reused to realize new customer requirements. Hypothesis: Given that the customer requirements are linked to product line features' description satisfying them: then the customer requirements can be clustered based on patterns and similarities, preserving the historic reuse information. New customer requirements can be evaluated against existing customer requirements and reuse of product line features can be recommended. Contributions: We treated the problem of feature reuse analysis as a text classification problem at the requirements-level. We use Natural Language Processing and clustering to recommend reuse of features based on similarities and historic reuse information. The recommendations can be used to realize new customer requirements.
Passive seismic signal denoising using convolutional neural network

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Passive seismic signal denoising using convolutional neural networkAuthors: Nam PhamDmitrii MerzlikinSergey FomelYangkang ChenNam PhamUniversity of Texas at AustinSearch for more papers by this author, Dmitrii MerzlikinUniversity of Texas at Austin, and SchlumbergerSearch for more papers by this author, Sergey FomelUniversity of Texas at AustinSearch for more papers by this author, and Yangkang ChenZhejiang UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3427266.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe propose a method for earthquake denoising using complexvalued convolutional neural network. We use UNet architecture with complex inputs from local time-frequency transform (LTFT). We use a modified LTFT which applies a smoothing operator along the frequency axis to obtain a higher temporal resolution. We add dropout layers to quantify the model uncertainty. We train the network on synthetic earthquakes and then apply it to field data. Applying the model parameters trained on synthetic data to real earthquakes can efficiently recover signals of different phases. Model uncertainties are related to waveform complexities of different arrivals.Presentation Date: Wednesday, October 14, 2020Session Start Time: 1:50 PMPresentation Time: 3:55 PMLocation: 351FPresentation Type: OralKeywords: machine learning, passive noise suppression, earthquakePermalink: https://doi.org/10.1190/segam2020-3427266.1FiguresReferencesRelatedDetailsCited byAdapting the residual dense network for seismic data denoising and upscalingRongqian Wang, Ruixuan Zhang, Chenglong Bao, Lingyun Qiu, and Dinghui Yang16 June 2022 | GEOPHYSICS, Vol. 87, No. 4 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Nam Pham, Dmitrii Merzlikin, Sergey Fomel, and Yangkang Chen, (2020), "Passive seismic signal denoising using convolutional neural network," SEG Technical Program Expanded Abstracts : 1581-1585. https://doi.org/10.1190/segam2020-3427266.1 Plain-Language Summary Keywordsmachine learningpassive noise suppressionearthquakePDF DownloadLoading ...
Information Processing System Design for Multi-rotor UAV-Based Earthquake Rescue

A design method of an information processing system for the multi-rotor unmanned aerial vehicle (UAV)-based earthquake rescue application is proposed. As a kind of new rescue measurement in the third medical center of the People’s Liberation Army (PLA) general hospital, this system is designed for the UAV of calamity rescue team. Four key functions are developed: the environment perception-based image enhancement, the homography matrix estimation-based image mosaicing, the imaging earthquake damage degree (EDD) evaluation, and the rescue plan recommendation. The blind image quality evaluation metrics are utilized to percept the changes of imaging environment. The homography matrix is employed to carry out the image mosaicing. Lots of image texture features are computed to assess the imaging EDD. The rescue plan is recommended by the backpropagation neural network (BPNN) with the considerations of EDD and other factors such as the population size or the weather condition. The practical tests have verified the effectiveness of the proposed system and method.
Composing Bossa Nova by Evolutionary Computation

Development of artificial intelligence has enabled automation of diverse complicated tasks, such as planning, scheduling, and natural language processing. As one of the complicated tasks, music composition involves various aspects, e.g., melody, rhythm, harmonization, phrasing, and forms. Evolutionary composition systems ordinarily formulate music composition as an optimization problem and adopt evolutionary algorithms to deal with it. This study focuses on evolutionary composition on a special genre-Bossa Nova. The proposed system uses integer-coded genetic algorithm to generate the melody, followed by a postprocess for arranging suitable accompaniment. In particular, the genetic algorithm features evaluation rules derived from music theory for consonant melodies. The postprocess creates a treble part and a bass part which together form the chord accompaniment on the basis of Bossa Nova harmony and rhythmic patterns. This study conducts experiments for quality verification of the generated music. Experimental results show that the proposed rules are able to guide genetic algorithm for composing harmonious melodies, and the accompaniments from postprocess blend well with the generated melodies. By and large, the integration of generated melodies and accompaniments presents satisfactory Bossa Nova music.
Normal-Wishart Clustering for Novelty Detection

Novelty detection is an important building block of self-improving system integration since it establishes a kind of self-awareness in the perception. This can be used, for instance, to detect novel processes or subsystems that have an impact on the efficiency of the integration state. CANDIES is a particular instance from the field of novelty detection that has been shown to fulfil the specific requirements of self-integration. In this article, we introduce a novel approach to online clustering of samples based on Bayesian methods that replace the spatial-density clustering approach which is currently used in CANDIES. This novel approach is called Normal-Wishart-Clustering. We analyse the accuracy of the approach in comparison to the standard setup of CANDIES in an example scenario and demonstrate the advantages.
StrengthGaming: Enabling Dynamic Repetition Tempo in Strength Training-based Exergame Design

Strength training improves overall health, well-being, physical appearance, and sports performance, with training programs specifying variables such as sets, repetitions, rest time, weight, and tempo. The repetitive nature of strength training, typically performed at fixed tempo, has made it challenging to develop entertaining exergames for common strength training exercises. We present StrengthGaming, which uses scaling and shuffling technique to provide tempo variations to strength training while preserving the training volume and training goals. It affords game designers more flexibility in designing strength training-based exergames. We developed a prototype game, inspired by FlappyBird, that uses a wearable orientation sensor to track the repetition tempo to control a flying character, using both fixed tempo design and dynamic tempo design. Results from our 24-person user study showed that dynamic tempo was significantly more entertaining than fixed tempo (p<0.01), and was preferred by participants.
Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset

Recently, there has been a growing interest in wearable sensors which provides new research perspectives for 360 ° video analysis. However, the lack of 360 ° datasets in literature hinders the research in this field. To bridge this gap, in this paper we propose a novel Egocentric (first-person) 360° Kinetic human activity video dataset (EgoK360). The EgoK360 dataset contains annotations of human activity with different sub-actions, e.g., activity Ping-Pong with four sub-actions which are pickup-ball, hit, bounce-ball and serve. To the best of our knowledge, EgoK360 is the first dataset in the domain of first-person activity recognition with a 360° environmental setup, which will facilitate the egocentric 360 ° video understanding. We provide experimental results and comprehensive analysis of variants of the two-stream network for 360 egocentric activity recognition. The EgoK360 dataset can be downloaded from https://egok360.github.io/.
Reproductive System Augmented Reality Application for Sexual Health Classes

This study aims to evaluate a reproductive systems augmented reality (AR) application for use in sexual health education. AR material included learner interactivity and a 3D video. Sixteen counselor candidates voluntarily registered to the sexual health class. A Reproductive Systems Questionnaire and an AR interview form were used to collect data. The participants were evaluated with a pretest, post-test and retention test. A significant improvement from pretest to post-test indicated that the AR application enhanced participants’ knowledge of reproductive organs and their positions. Participants learned more about female reproductive systems than male reproductive systems.
Human Activities Recognition from Videos Based on Compound Deep Neural Network

The two-stream deep neural networks learn contour evolution information of human activities from image frames and motion information from information evolution between image frames. Optical flow image is one of the commonly-used methods to depict the motion information between frames. The existing optical flow images are usually obtained by off-line calculations. In this paper, a deep neural network named FlowNet2.0 is used to generate optical flow images, and based on which a compound deep neural network is proposed to merge spatial and temporal information to complete the recognition of human activities from videos. Specifically, the proposed compound deep neural network is composed of two sub-networks, static-data-stream learning network and dynamic-data-stream learning network. The former is applied to extract spatial evolution information from RGB images, and the latter generates optical flow prediction images from RGB image sequence first, and extracts temporal evolution information from optical flow images. Finally, the results of two sub-networks are combined under the fusion algorithm to achieve human activities recognition. Experimental results show that the compound deep neural network proposed in this paper can effectively identify human activities from video sequences, and the average classification accuracy on data set UCF101 can reach 83.35%.
An NLP tool for decoding the ATC Phraseology from English to Bengali

In recent times, the translation and transliteration capabilities are growing at an unprecedented rate. The increasing research in machine translation [MT] has led to the development of more powerful real-time translation systems. Most of the neural machine translation [NMT] and statistical machine translation [SMT] systems are mainly dependent on the parallel corpus, which has been used to train the model. As the applications of machine translation [MT] methods are more focused on agriculture, tourism, health care and other such smart applications. Its implementation lags in certain other domains like aviation, aero-space and so on. Though many SMT and NMT systems exist today, still in lacks to perfectly translate and transliterate the sentences related to aviation. As a result, the aviation related translation and transliteration tools requires significant research attention. Hence, this paper proposes a Natural Language Processing [NLP] tool to decode the required Air Traffic Controllers [ATC] phraseology from English to a local language Bengali.
An improved PSO algorithm for solving nonlinear programing problems with constrained conditions

Engineering optimization problems can be always classified into two main categories including the linear programming (LP) and nonlinear programming (NLP) problems. Each programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized system. This paper will focus on the issue about the design problem of NLP with the constrained conditions. The employed method for such NLP problems is a variant of particle swarm optimization (PSO), named improved particle swarm optimization (IPSO). The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization problems. In this work, many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO algorithm. Simulation results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions.
P-SaaS: knowledge service oriented manufacturing workflow model for knowledge collaboration and reuse

Orders come and go, but for manufacturing enterprises product-related production problems (PPs) are forever. To realize the aim of CTQS (i.e., lower cost, faster time to market, higher quality and better service), a serviced oriented model of product related problem-solving as a service (P-SaaS) is presented with three-layer structure: task bade workflow layer, knowledge based solution layer and cognition based decision-making layer in this paper. For knowledge oriented PPs solving, each task/subtask is taken as a piece of knowledge service, and the function based task decision for problem solving is organized and implemented by independent Web applications on the microservice architecture platform. Each application is considered as an agent based on its independent function. To support this model, operational models and key enable technologies are detailed. Firstly, a task based workflow is constructed to orchestrate the meta-events of specific task through coordination agent. Secondly, focused on a specific task of PPs query, a similarity based knowledge flow is proposed to retrieve the finite set of alternative solutions with a set threshold value through core agent. Thirdly, a cognition based decision synthesis flow is used to improve the quality of alternative solutions by knowledge collaboration and fusion through the individual agent. Finally, a case on high voltage apparatus in XD company is introduced to validate the proposed models.
Train-level Fault Diagnosis based-on Feature Selection

In this paper, a train-level fault diagnosis optimization method based on feature selection is proposed. Based on historical operational data, the feature space of train-level fault diagnosis is established. An improved support vector machine (SVM)-based feature selection method is used according to feature contribution. The value is screened out as a diagnostic parameter for train-level faults, and train-level fault diagnosis is optimized. In this paper, taking the train-level traction severe fault as an example, combined with the actual operational data, the intrinsic relationship between the fault and the train signals is studied to optimize the train-level fault diagnosis.
Towards High-Level Synthesis of Polymorphic Side-Channel Countermeasures

Side-channel attacks pose a severe threat to both software and hardware cryptographic implementations. Current literature presents various countermeasures against these kinds of attacks, based on approaches such as hiding or masking, implemented either in software, or on register-transfer or gate-level in hardware. However, emerging trends in hardware design lean towards a system-level approach, allowing for faster, less errorprone, design process, an efficient hardware/software co-design, or sophisticated validation, verification, and (co)simulation strategies. In this paper, we propose a Boolean masking scheme suitable for high-level synthesis. We implement a protected PRESENT encryption in C language, utilizing the concept of dynamic logic reconfiguration, synthesize it for Xilinx Artix 7 FPGA, and we compare our results regarding clock cycle latency and area utilization. We evaluate the effectiveness of proposed countermeasures using specific t-test leakage assessment methodology. We show that our high-level synthesis implementation successfully conceals the side-channel leakage while maintaining reasonable area and latency overhead.
The Digital Crowd

Although digital technologies are implicated in intrusive surveillance and social fragmentation, their widespread uses in recent political protests demonstrate their potentials to support intense forms of collective life. Building on Mark Hansen’s new-media philosophy, this paper explores digital crowds as an intensified form of a contemporary phenomenon: the intertwinement of digital media with social life. Enveloped in technospheres of data-rich devices, the digital crowd forms hybrids with its environment of distributed digital intelligence, what Hansen conceptualizes as System-Environment Hybrids. Sampling the visible trace of such SEHs on Instagram, the article posits that their impacts on crowd formations signal a distinct form of collective life. It argues that the hybrid intensifies the affect and de-individuation processes of conventional pre-digital crowds, and extends such effects well beyond crowd events into persistent online environments of insatiable exchange. The paper speculates that intensified and persistent affect transform the emotional geography of the city.
Advanced Data Preprocessing for Detecting Cybercrime in Text-Based Online Interactions

Social media provides a powerful platform for individuals to communicate globally. This capability has many benefits, but it can also be used by malevolent individuals, i.e. predators. Anonymity exacerbates the problem. The motivation of our work is to help protect our children from this potentially hostile environment, without excluding them from utilizing its benefits.
Improved publicly verifiable auditing protocol for cloud storage

Summary Outsourcing data to cloud servers is a popular service for data owners, however, how to check the integrity and freshness of the outsourced data is very challenge. Recently, Jin et al. proposed a cloud auditing protocol with full integrity and freshness support for cloud data, unfortunately in this article, we show their proposal is not secure. Concretely, the cloud servers can forge the authentication tag and thus has the ability to forge proof of data possession, which obviously invalidates their cloud auditing protocol. We also give a new cloud auditing protocol and analysis its security and performance. The results show our protocol is more efficient and secure.
Improving Pathological Distribution Measurements with Bayesian Uncertainty

Deep learning assisted histopathology has the potential to extract reproducible and accurate measurements from digitised slides in a scalable fashion. A typical workflow of such analysis may involve instance segmentation of relevant tissues followed by feature measurements. Inherent segmentation uncertainties produced by these deep models, however, could propagate to the downstream measurements, causing biased distribution estimate of the whole slide. One challenging aspect when handling ambiguous tissues is that the number of instances could differ as the instance segmentation step may not generalise well to these tissues. As an attempt to address this problem, we propose to derive a confidence score from the segmentation uncertainties obtained from Bayesian Neural Networks (BNNs) and utilise these as weights to improve the distribution estimate. We generate a synthetic dataset that mimics the diverse and varying visual features of the original data to enable systematic experiments. With this dataset we demonstrate the robustness of the method by extracting several clinically relevant measurements with two different BNNs. Our results indicate that the distribution estimates are consistently improved when the instances are weighted by the entropy-derived confidence measure. In addition, we provide results on applying the method to the original data.
DomainPKI

Certificate Authority (CA) is a single point of failure in the trust model of X.509 Public Key Infrastructure (PKI), since CA is the only entity to sign and distribute public key certificates and no one else is involved in certificate verification. In response, recent fixes based on public logs have been successful in making certificate management more transparent and publicly verifiable. However, more recent researches have shown that none of existing solutions is fully satisfactory due to different security flaws and operational challenges. In this study, we propose a domain-aware alternative to mitigate those issues by involving domain owner in digital signature and certificate verification.
The Application of Ensemble Learning on Named Entity Recognition for Legal Knowledgebase of Properties Involved in Criminal Cases

Entities are important units that carry information in texts and the core units of the knowledgebase. Named entity recognition (NER) plays a key role in the construction of knowledgebase. In this paper we explore the NER problem for legal knowledgebase of properties involved in criminal case. In the scenario with limited training corpus and high recognition performance requirements, parallel ensemble learning is introduced on the basis of four NER methods. Experimental results show that ensemble learning can further improve the accuracy of recognition.