Reviewerly/exHarmony huggingface dataset:
Accelerating DNN Inference by Edge-Cloud Collaboration

Deep neural networks (DNN) have become indispensable tools for intelligent applications today. The demand for deploying DNN on the edge devices increases dramatically. Unfortunately, it is challenging because the DNN inference is computation-intensive, but edge devices are always resource-constraint. Prior solutions attempted to address these challenges with collaboration between cloud and edge devices, but they do not take the inference request rate into account. However, the inference delay will increase dramatically while the request rate becomes higher.In this paper, we propose a scheme to dynamic partition DNN into two or three parts and distribute them at the edge and cloud, achieving the lowest delay with the change of request rate. The scheme selects the optimal partition points of DNN with a layer evaluation model (LEM) and a total delay prediction model (DPM) under different request rates. The experiments of distributed deploying AlexNet, VGG, NiN and ResNet DNN models on image classification dataset ImageNet show that the proposed scheme significantly reduces the total end-to-end latency by fully using both the edge and cloud resources. It reduces the inference delay by 1.3 to 1.6 times and improves the throughput 1.2 to 1.7 times compared to the state of art partition approach.
Blockchain-Based Secure Biomedical Data-as-a-Service for Effective Internet of Health Things Enabled Epidemic Management

The Medical Infrastructure-as-a-Service (MIaaS) allows the healthcare providers to share the often expensive and rare smart medical infrastructures to deal with a significant number of patients requiring medical expertise and achieve accurate and rapid diagnosis and treatment. However, in epidemics, many of these computational intelligence-based diagnosis systems require up-to-date trained machine learning models, which are dependent on receiving current data. In this chapter, the concepts of Biomedical Data as a Service (BDaaS) for providing real-time data for training the medical machine learning models, Model Training as a Service (MTaaS) for providing off-site training of machine learning models on Internet of Health Things (IoHT) and Computational-Intelligence-as-a-Service (CIaaS) for using the trained models are proposed and investigated, and a blockchain-based framework for secure BDaaS+MTaaS+CIaaS is proposed. The advantages of decentralized BDaaS+MTaaS+CIaaS are security, agility, cost-effectiveness, data quality, computational intelligence quality, data equality and computational intelligence equality. The proposed framework uses the blockchain network for secure decentralized transfer and sharing of biomedical data and machine learning models on IoHT. Different scenarios exploring the complex dynamics of COVID-19 pandemic and the applications of the proposed framework are investigated.
Surveying and Analyzing Privacy Issues in Contact tracing apps

The coronavirus pandemic continues to represent widespread panic, and global initiatives are focused on treating and limiting its spread. Contact tracing promises to help fight this pandemic through early detection of possible contagion events. In this context, several mobile applications have been proposed to try to identify the contact cases of a person who tested positive for COVID-19. In total, 32 countries have developed such applications and are using them as a strategy that may flatten the pandemic curve. These applications can also be adopted to combat future communicable diseases among humans. However, most of these applications do not fully respect the preservation of the privacy of users. That’s why, the adoption of these applications is being held back in many countries. Motivated by the need to protect user privacy, we analyze some of these applications. We demonstrate that they collect sensitive data which represents a violation of user privacy. At the end, we present several major recommendations that can serve as a reference, contributing to the development of privacy-preserving contact tracing applications.
Short-wave infrared continuous-variable quantum key distribution over satellite-to-submarine channels

The trans-media transmission of quantum pulse is one of means of free-space transmission which can be applied in continuous-variable quantum key distribution (CVQKD) system. In traditional implementations for atmospheric channels, the 1500-to-1600-nm pulse is regarded as an ideal quantum pulse carrier. However, the underwater transmission of this pulses tends to suffer from severe attenuation, which inevitably deteriorates the security of the whole CVQKD system. In this paper, we propose an alternative scheme for implementations of CVQKD over satellite-to-submarine channels. We estimate the parameters of the trans-media channels, involving atmosphere, sea surface and seawater and find that the short-wave infrared performs well in the above channels. The 450-nm pulse is used for generations of quantum signal carriers to accomplish quantum communications through atmosphere, sea surface and seawater channels. Numerical simulations show that the proposed scheme can achieve the transmission distance of 600 km. In addition, we demonstrate that non-Gaussian operations can further lengthen its maximal transmission distance, which contributes to the establishment of practical global quantum networks.
A Dendritic Neuron Model for Breast Cancer Classification

Breast cancer is the most common malignant tumor among women, and the incidence is on the rise all over the world, endangering women's life and health seriously, the prevention and treatment of this disease becomes important. Aiming at the classification problem of breast cancer, this paper uses the dendritic neuron model to mine and analyze the data, and determines the effective learning algorithm of the model for breast cancer data through comparative experiments. The experimental data were collected from two common breast cancer classification datasets in UCI database, and six classical learning algorithms were compared. The experimental results show that the dendritic neuron model has high accuracy, fast convergence, and remarkable classification performance for breast cancer.
Property-based Test for Part-of-Speech Tagging Tool

Part-of-Speech (POS) tagging for sentences is a basic and widely-used Natural Language Processing (NLP) technique. People rely heavily on it to predict POS tags that serve as the base for many advanced NLP tasks, such as sentiment analysis, word sense disambiguation, and information retrieval. However, POS tagging tools could make wrong predictions, which bring consequent error propagation to the advanced tasks and even cause serious threats in critical application domains. In this paper, we propose to test POS tagging tools with Metamorphic Testing against some properties that they should follow. The preliminary exploration with two groups of Metamorphic Relations shows that our method can effectively reveal defects of three common POS tagging tools (i.e., spaCy, NLTK, and Flair) on handling fairly simple intra- and inter-sentence transformation regarding adverbial clause and sentence appending. This demonstrates the great potential of our method to deliver a systematic test and reveal the unaware issues, which may benefit the validation, repair, and improvement, for POS tagging tools.
PIDLNet: A Physics-Induced Deep Learning Network for Characterization of Crowd Videos

Human visual perception regarding crowd gatherings can provide valuable information about behavioral movements. Empirical analysis on visual perception about orderly moving crowds has revealed that such movements are often structured in nature with relatively higher order parameter and lower entropy as compared to unstructured crowd, and vice-versa. This paper proposes a Physics-Induced Deep Learning Network (PIDLNet), a deep learning framework trained on conventional 3D convolutional features combined with physics-based features. We have computed frame-level entropy and order parameter from the motion flows extracted from the crowd videos. These features are then integrated with the 3D convolutional features at a later stage in the feature extraction pipeline to aid in the crowd characterization process. Experiments reveal that the proposed network can characterize video segments depicting crowd movements with accuracy as high as 91.63%. We have obtained overall AUC of 0.9913 on highly challenging publicly available video dataset. The method outperforms existing deep-learning frameworks and conventional crowd characterization frameworks by a notable margin.
A Knowledge-aware Machine Reading Comprehension Framework for Dialogue Symptom Diagnosis

Symptom diagnosis in dialogue remains a challenging task because the symptom entities and their status need to be extracted correctly at the same time. Most previous studies treat symptom diagnosis as a classification or sequence labeling task and focus on using single-sentence dialogue as input. Unique from past studies, in this paper, we propose a new framework for dialogue symptom diagnosis, which formulate it as a machine reading comprehension (MRC) task. We first use window-level multi-turn of dialogue as input and extract the symptom entities. Then, we generate a question for each entity to infer the symptom status in the form of question answering (QA). Benefit from the MRC formalization, our proposed framework can encode more informative prior knowledge, which can effectively improve the performance of symptom status inference. Experiments on the Chinese medical dialogue dataset show that the proposed framework outperforms the previous best model and several competitive baselines, which indicates that our framework provides a useful direction for dialogue symptom diagnosis. The code and data are publicly available at https://github.com/zhaoxiongjun/DSD.
A study on concrete beam with profiled reinforcement

In this research focus with different types of profiled main reinforcement in RCC Beam. In this research work undertake two different types of reinforced profiles tested, one is parabolic profile and another one wave profile main reinforcements. This study analyses deflection and strength of the reinforced concrete beam with the above two different types of profiles. The RCC beam shows different behavior at the failure stage. Cost and material used for ordinary profile reinforced beam made to study the alternatives. The reinforcement made around the same cost will give more strength than that of ordinary profile reinforced beam. It will give less deflection than the conventional beam. The test results of strength and deflection of the different profiled reinforcement beam listed and compared with ordinary profile reinforcement beam. In deflection test results of different profiled RCC beam measured increasing of loads.
A Method to Extract Rail Flatness Profile and Analysis of Different Noise Sources

This paper addresses the problem of the rail flatness assessment that occurs at its final production stage. Flatness control must be measured during the total length of each rail to avoid problems during this rail usage. Only the flat rails can be used in high-performance environments such as high-speed trains. Rails quality assessment must be done to avoid highway security problems or any other type of circumstances that may prevent the normal operation of the systems where these rails will be installed. The rail that does not meet the minimum flatness requirement for the high-performance usage, can be used in standard railways, but if the flatness imperfections are greater than certain tolerances, it must go through a process of re-straightening or been discarded.To obtain the rail points where flatness will be measured we propose a method based on a set of cameras distributed in a range along a section of the rail. The goal is, using a set of cameras, reduce the impact of the rail vibrations and then, obtain a synthetic shape that calculates original rail shape. This shape can be used to measure rail flatness using any other process. The obtained results indicate that this method is robust and accurate.
ICT and Digitization in the United States: Research, Trends, and Issues

This chapter aims at assisting and guiding educators, researchers, and policymakers who are interested in learning about Information and Communication Technology (ICT) and digitization in education in the United States (USA). With a historical context covering the past 20 years, the chapter provides an overview of research, trends, and issues related to ICT and digitization in K-12 school settings. It presents a wide range of developments in ICT and digitization impacting the field of public education, exploring and analyzing the following four key issues: what technology integration is and what isn’t; why technology integration is important; emerging technologies in education; and funding for technology and the “digital divide.” The chapter ends describing the most recent stage on the key issues discussed and the implications for policy and practice these issues introduce.
Abnormality Detection and Segmentation in Breast Digital Mammography Images Using Neural Network

Early diagnosis of breast cancer increases the patient's chances of survival. In the last two decades many Computer Aided Detection(CAD) systems were developed to help radiologist analyse digital mammograms. The current state-of-the-art systems use YOLO, Faster R-CNN and other classification and detection algorithms to detect masses and calcification present in the mammograms. The training process in these systems is complex, time consuming and requires large amount of data. The heterogeneous nature of the data with irregular shapes and sizes makes the task more difficult. In the present paper we illustrate the development of a deep learning study aimed to detect masses, calcification and other irregularities in digital mammography images using Anomaly Detection and Localization. The proposed model has been implemented using two publicly available datasets and satisfactory results have been achieved.
Ignoring Qualifications as a Pragmatic Fallacy: Enrichments and Their Use for Manipulating Commitments

The fallacy of ignoring qualifications, or secundum quid et simpliciter, is a deceptive strategy that is pervasive in argumentative dialogues, discourses, and discussions. It consists in misrepresenting an utterance so that its meaning is broadened, narrowed, or simply modified to pursue different goals, such as drawing a specific conclusion, attacking the interlocutor, or generating humorous reactions. The “secundum quid” was described by Aristotle as an interpretative manipulative strategy, based on the contrast between the “proper” sense of a statement and its meaning taken absolutely or in a certain respect. However, how can an “unqualified” statement have a proper meaning different from the qualified one, and vice versa? This “linguistic” fallacy brings to light a complex relationship between pragmatics, argumentation, and interpretation. The secundum quid is described in this paper as a manipulative argument, whose deceptive effect lies in its pragmatic dimension. This fallacy is analyzed as a strategy of decontextualization lying at the interface between pragmatics and argumentation and consisting of the unwarranted passage from an utterance to its semantic representation. By ignoring the available evidence and the presumptive interpretation of a statement, the speaker places it in a different context or suppresses textual and contextual evidence to infer a specific meaning different from the presumable one.
Model Selection Criterion for Multivariate Bounded Asymmetric Gaussian Mixture Model

In this paper, model selection criterion for bounded support asymmetric Gaussian mixture model (BAGMM) using minimum message length (MML) is proposed. The proposed approach is validated using synthetic data, real data and occupancy detection application. The proposed approach is compared with other state of the art model selection approaches. Moreover, the developed bounded mixture is compared with asymmetric Gaussian mixture model (AGMM).
A Fast and Robust UAV Images Mosaic Method

Unmanned Aerial Vehicle (UAV) can respond quickly and provide image data at the first time in mapping support tasks, such as battlefield environment monitoring and natural disaster damage assessment. However, UAV is prone to fast flight speed and poor attitude stability, which brings a new challenge to image mosaic relying on feature matching. In this paper, an efficient and robust UAV image mosaic method is proposed. The main idea is to take graph theory and POS data to image mosaic process. Firstly, we estimate the range of aerial photography based on POS data, and construct the UAV images relationship diagram. Then, according to the attitude information, the images in relation set are divided into stable group and disturbance group, and the image classification connection graph is generated. By defining the weighted topological graph and constructing the minimum spanning tree, we search key frames and reference image. Finally, we derive the homography matrix between adjacent images according to imaging model. The mosaic image is generated after global registration and images fusion. Experimental results show that our proposed approach can solve the mosaic problems of narrow overlap and texture deficient images, and produce superior results in reducing projection distortion and shortening registration time consumption.
Research and Application of Automated Search Engine Based on Machine Learning

At present, most search engines mostly use keyword matching mode, without considering the inherent semantics of the text, resulting in low accuracy and recall rates of search results. In order to solve this problem, topic models have been widely used in information retrieval due to their advantages in mining document hidden concepts, but there are still the following problems: First, its unsupervised learning form causes the generated topics to be unclear in interpretability; Second, the topic model is more suitable for processing long texts, and is not suitable for query sentences with fewer features. In order to solve the above problems, this paper proposes an intelligent retrieval method based on topic model based on the semantic characteristics of intelligent retrieval. This method conducts research from two perspectives of document retrieval model and user questioning. It uses topic model and clustering algorithm to mine the latent semantics of documents and constructs a document index. It uses ontology technology to process user question sentences and standardize retrieval styles. Firstly, expert knowledge is selected as the prior knowledge of the topic model, which improves the defect that the topic of the unsupervised topic model deviates from the actual semantics, realizes the semi-supervised topic modeling, and generates the document topic tags that meet the characteristics. Secondly, it studies the clustering method of documents, combines Bagging ensemble learning ideas, and improves the traditional k-means algorithm to solve the problem of sparse text topic distribution generated by topic model training on the original corpus. Third, construct the domain ontology, describe the logical relationship between each entity, and complete the persistence of the ontology, identify the semantic and logical relationship of the query sentence in the retrieval phase, reconstruct the user retrieval conditions, and discover the user's real retrieval requirements; finally, according to The comprehensive matching rate of the query and the document, and the result list is sorted according to the similarity to improve the retrieval accuracy and recall rate.
Eliciting Confusion in Online Conversational Tasks

Confusion is a complex affective experience that involves both emotional and cognitive components, being less conspicuous than core emotions such as anger or sadness. We discuss an online data collection study designed to elicit confusion in spontaneous conversations across two dialogue tasks. Results from an analysis of the multimodal data (transcribed spoken language and facial expressions) suggest that the tasks induced naturalistic confusion, towards automated confusion recognition.
Signed Distance Function based Geofencing for UAV Corridor

With increase in urban surface level traffic congestion, development of urban air mobility is attracting a lot of attention from researchers. Urban Air Mobility requires rules and regulations in a traffic scenario so as to deployUnmannedAerialVehicles (UAV) in urban areas. Geo-fencing is a critical component of the UAV system as it designates the strict boundaries of the lanes and sub-lanes for safety and smooth flow of UAV traffic. In this work, a novel approach for representation of geofencing using Signed Distance Functions (SDF) is presented. A geofence repulsion strategy, so that UAV does not collide with various proposed geofencing layers, and violation check using the trajectory planning strategy are proposed to ensure smooth flow of drones in the airspace. These methods are developed for CORRIDRONE, an architecture for authorizing drones in the controlled airspace, but are general enough to be used in other air mobility systems too.
SA(2,R) Multi-scale contour registration based on EM Algorithm

Here we propose a novel affine registration method for planar curves. It is based on a pseudo-inverse algorithm applied to the source and target curves in their multi-scale version. The proposed registration system selects the relevant scales in the optimized L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> distances. The retrieved smoothing parameters are realized with the Gaussian Expectation-Maximization (EM) algorithm. We resolve the global system, formed by equations corresponding to EM selected scales.
A Strong Baseline Based on Adaptive Mining Sample Loss for Person Re-identification

At present, deep learning has made great progress in Person re-identification task. Many advanced models can achieve high performance. However, only a few scholars pay attention to studying the baseline. We find that embedding the same module into different baselines can lead to huge performance gaps between models. Therefore, the baseline is an extremely important component of the model. By studying the existing methods, we designed a powerful baseline with a simple structure. The proposed baseline uses resnet-50 as the backbone to extract the global features of people. To mine the information of the samples and make the model converge quickly, we designed an Adaptive Mining Sample Loss (AMSL), which allocates weights adaptively based on the Euclidean distance between samples. Compared with the triple loss, AMSL can not only push the positive point pairs closer, concentrate the samples of each class into the hypersphere to prevent the structural information from being destroyed. Experimental results indicate that the mAP and Rank-1 of the proposed baseline are 19.0\(\%\) and 19.6\(\%\) higher than the most powerful baseline BagTricks on CUHK-03, respectively.