Reviewerly/exHarmony huggingface dataset:
Development of an Interactive Semantic Medical Image Segmentation System

Medical image segmentation (i.e., liver segmentation) is an important pre-processing for computer-aided diagnosis and computer-aided surgery. Deep learning, which can perform fully automated segmentation, has been popular in the medical field because of its ability to reduce the burden on doctors. However, segmentation is inherently a difficult problem and the accuracy of current deep learning-based segmentation methods remains insufficient. In this work, an interactive network-based segmentation is proposed, which can improve the operation through collaborations with a doctor.
Using Deep Learning Technique for Recovering the Lost Measurement Data

With the growing number of applications of structural health monitoring systems on civil engineering structures, different types of sensors have been used to collect both the vibration data and environmental conditions. Signal loss is an unavoidable issue during transmission that significantly affects the quality of signals and consequently affects the practical applications of structural health monitoring techniques. This paper proposes a novel approach using deep learning technique for recovering the lost measurement data. The used convolutional neural network is a fully feed-forward convolutional neural network with residual connections. It constructs the nonlinear relationships between the incomplete signal with data loss and the original signal by training with the prepared training datasets consisting of pairs of incomplete and original vibration signals. The trained network then extracts the robust higher representation features of the input incomplete signals layer by layer using the compression layers, and expands those features gradually throughout the reconstruction layers to recover the original signals. The effectiveness of the approach is evaluated by using the data from Dowling Hall footbridge. The network exhibits the good capability of lost data recovery, even when the signals have severe data loss proportion up to 90%.
A Text Analytics Framework for Performance Assessment and Weakness Detection From Online Reviews

Present research proposes a framework that integrates aspect-level sentiment analysis with multi-criteria decision making (TOPSIS) and control charts to uncover hidden quality patterns. While sentiment analysis quantifies consumer opinions corresponding to various product features, TOPSIS uses the sentiment scores to rank manufacturers based on their relative performance. Finally, U and P control charts assist in discovering the weak aspects and corresponding attributes. To extract aspect-level sentiments from reviews, we developed the ontology of passenger cars and designed a heuristic that connects the opinion-bearing texts to the exact automobile attribute. The proposed framework was applied to a review dataset collected from a well-known car portal in India. Considering five manufacturers from the mid-size car segment, we identified the weakest and discovered the aspects and attributes responsible for its perceived weakness.
mmFilter

Advances in deep learning have shown promising potential in scalable video analytics in the cloud. However, in constrained settings, it is not feasible to send every video frame from cameras at the edge. Being selective about which frames should be sent to the cloud reduces bandwidth consumption, conserves energy, and protects user privacy.
Evolutionary Neural Network and Visualization for CNN-based Pulmonary Textures Classification

Accurate classification and comprehensive explanation is crucial to build a computer aided diagnosis (CAD) system of diffuse lung disease (DLD). Although deep neural networks (DNNs) have been applied to this task, the classification performance and reliability are not satisfied for medical clinical requirements. Specifically, DNNs are regarded as unexplainable “black-box” in general, and, thus, are not deemed reliable by expects. In this paper, we propose a neural network structure search approach based on evolutionary algorithm to improve the DNN's effectiveness and interpretability, and applied to the pulmonary textures classification problem. Through this network structure search approach, we find out how a DNN's subnet recognize the pulmonary textures features, then filter out the redundant subnets, and retain the most distinctive feature subnets. Besides, we utilize the method of feature visualization and the fine-grained heat map of the activation to interpret network's decision-making process. Finally, through quantitatively and qualitatively evaluate on a real dataset of diffuse lung disease, we verify the effectiveness of this neural network structure search approach on VggNet and ResNet, and achieve the state-of-the-art performance. We can classify the pulmonary textures on high-resolution computed tomography (HRCT) images.
Research on coal mine safety monitoring data storage based on HBase

Abstract In order to solve the problem of low storage efficiency and slow query speed when massive coal mine safety monitoring data is stored in relational database, a method of storage and retrieval based on HBase was designed. By combining the hash value of the combination of coal mine code, substation code and transducer code as well as the timestamp after inversion, the hot spot and data skew caused by the unreasonable design of rowkey in HBase were avoided. In order to improve the efficiency of data storage, the swing door trending (SDT) algorithm was used to compress the monitoring data, and in order to improve the efficiency of data retrieval, a secondary index based on ElasticSearch was constructed. The experimental results on the real coal mine data show that the efficiency of data storage and retrieval has been significantly improved.
Scalable and Hybrid Ensemble-Based Causality Discovery

Causality discovery mines cause-effect relationships among different variables of a system and has been widely used in many disciplines including climatology and neuroscience. To discover causal relationships, many data-driven causality discovery methods, e.g., Granger causality, PCMCI and Dynamic Bayesian Network, have been proposed. Many of these causality discovery approaches mine time series data and generate a directed causality graph where each graph edge denotes a cause-effect relationship between the two connected graph nodes. Our benchmarking of different causality discovery approaches with real-world climate data shows these approaches often generate quite different causality results with the same input dataset due to their internal learning mechanism differences. Meanwhile, there are ever-increasing available data in virtually every discipline, which makes it more and more difficult to use existing causality discovery algorithms to produce causality results within reasonable time. To address these two challenges, this paper utilizes data partitioning and ensemble techniques, and proposes a two-phase hybrid causality ensemble framework. The framework first conducts phase 1 data ensemble for partitioned data and then conducts phase 2 algorithm ensemble from data ensemble results. To achieve scalability, we further parallelize the ensemble approaches via the Spark big data analytics engine. Our experiments show that our proposed approaches achieve good accuracy through ensemble and high scalability through data-parallelization in distributed computing environments.
A level‐set method for inhomogeneous image segmentation with application to breast thermography images

Various level-set methods have been suggested for segmenting images with intensity inhomogeneity as local region-based models. The challenge in these methods is segmenting the inhomogeneous images with smooth edges. These methods cannot properly segment regions with smooth edges in inhomogeneous images. This paper presents a new local region-based active contour model called local self-weighted active contour model. In the proposed method, a novel different weighting technique is applied. In this model, the weight of each neighbour pixel in the energy function is set by a function of its intensity and not its geometrical distance regarding the central pixel as previous methods. Considering this, the presented approach can segment regions with smooth edges in the presence of inhomogeneity as breast thermography images. The experimental results of applying the model on heterogeneous images containing synthetic images and medical images, especially breast thermography images, are compared with well-known local level-set methods which show the perfect capability of the model. The segmentation results were evaluated using the F-score, accuracy, precision and recall criteria. The results show values of 0.8, 0.62, 0.73 and 0.82 for the average accuracy, F-score, precision and recall criteria on the segmentation of breast thermography images, respectively.
Convolutional Neural Network for Classification for Indian Jewellery

The classification of jewellery involves the identification of a jewellery among several jewellery images. The field has application is e-commerce. In the present paper, a deep convolutional neural network has been trained in a fully supervised manner on a dataset of Indian jewellery images. The network would able to classify a given query image of a jewellery to the multiple categories of the pre-trained categories of the network with the respective probabilities of each category. A classification accuracy of 88% has been achieved with the network.
A Prediction Based Replica Selection Strategy for Reducing Tail Latency in Distributed Systems

In the distributed system, applications often suffer from unexpectedly high response time fluctuation which is a consequence of long-tail latency. One efficient way of reducing tail latency is to use an appropriate replica selection strategy. In this paper, through extensive experiments, we analyze and evaluate two popular state-of-the-art replica selection strategies of key-value store systems: Cassandra dynamic snitch and C3. After analyzing all the experimental results we develop a prediction based replica selection strategy and implement it on Cassandra 3.0. Our proposed algorithm outperforms both C3 and Cassandra dynamic snitch. For evaluation, we test Cassandra dynamic snitch, C3, and our proposed algorithm on a 15 nodes Cassandra cluster. For generating test datasets and workloads we use industry-standard Yahoo Cloud Serving Benchmark (YCSB).
Data Augmentation Using User Attention for Educational Content Recommendation Based on FFM

This paper proposes a method for augmenting evaluation data using each user's attention (view counts for each attribute of educational contents) for Field-aware Factorization Machines (FFM). In this study, we assume that the target user has no evaluation data. In the proposed method, by using the target user's attention, we retrieve a similar user from active users. Then, the evaluation data of the active user is duplicated for the target user. By training FFM using the duplicated evaluation data, personalized recommendation for the target user becomes feasible. Experimental results using educational contents on Forestanet show the effectiveness of the proposed method.
Noise-robust Attention Learning for End-to-End Speech Recognition

We propose a method for improving the noise robustness of an end-to-end automatic speech recognition (ASR) model using attention weights. Several studies have adopted a combination of recurrent neural networks and attention mechanisms to achieve direct speech-to-text translation. In the real-world environment, however, noisy conditions make it difficult for the attention mechanisms to estimate the accurate alignment between the input speech frames and output characters, leading to the degradation of the recognition performance of the end-to-end model. In this work, we propose noise-robust attention learning (NRAL) which explicitly tells the attention mechanism where to "listen at" in a sequence of noisy speech features. Specifically, we train the attention weights estimated from a noisy speech to approximate the weights estimated from a clean speech. The experimental results based on the CHiME-4 task indicate that the proposed NRAL approach effectively improves the noise robustness of the end-to-end ASR model.
An Experimental Study on Reinforcement Learning on IoT Devices with Distilled Knowledge

This paper provides an experimental study of reinforcement learning on IoT devices using distilled knowledge, whose a teacher with a well-trained model transfers to a student with a new model to be trained. The experimental results show that the distilled knowledge is effective to a new model training on IoT devices.
A Pilot of Student-Guided Virtual Reality Tours

Construction management programs that contain technology courses often teach students how to create building information models (BIM). The problem is that creating these models for the first few times is a difficult process. This learning process pushes students’ spatial abilities to the limit as they try to understand how buildings come together while trying to perfect their final product. However, when students can view their models at a 1:1 scale instead of on a flat computer screen, they start seeing things differently. Recently, nearly 60 students took part in completing their regularly assigned BIM project in a construction technology class. With the aid of an Oculus Go® virtual reality headset, students walked in their finished product and were able to critique their work from a different perspective. Furthermore, this pilot placed the student’s in their model along with their classmates. This way, the author of the model could take their classmates on a virtual tour of their work, allowing multiple people to review and critique the finished product. This paper describes a pilot inquiry into the use of collaborative virtual reality in a 4-year construction management classroom to improve student’s building information modeling skills. This paper presents the students’ feedback about the experience and documents the researcher’s observations in preparation for a plenary study on collaborative virtual reality in the classroom.
Improving Robustness of DNNs against Common Corruptions via Gaussian Adversarial Training

Deep neural networks have demonstrated tremendous success in image classification, but their performance sharply degrades when evaluated on slightly different test data (e.g., data with corruptions). To address these issues, we propose a minimax approach to improve common corruption robustness of deep neural networks via Gaussian Adversarial Training. To be specific, we propose to train neural networks with adversarial examples where the perturbations are Gaussian-distributed. Our experiments show that our proposed GAT can improve neural networks' robustness to noise corruptions more than other baseline methods. It also outperforms the state-of-the-art method in improving the overall robustness to common corruptions.
Low-Power Implementation of a High-Throughput Multi-core AES Encryption Architecture

Nowadays, the Internet of Things (IoT) has been a focus of research that improves and optimizes our daily life based on intelligent sensors and smart objects working together. Thanks to Internet Protocol connectivity, devices can be connected to the Internet, thus allowing them to be read, controlled, and managed at any time and at any place. Security and privacy are the key issues for deploying IoT applications, and still face some enormous challenges; especially, for devices that require high throughput and low latency as IoT cameras, IoT gateways, high-quality video conferencing systems... In this paper, we proposed a 10-cores AES hardware architecture to achieve high throughput. These cores shared KeyExpansion Block so this architecture has high efficiency in term of area and power consumption. Fully parallel, outer round pipeline technique is also used to achieve low latency. The design has been modelled in RTL VHDL and then synthesized with a 45nm CMOS technology using Synopsys Design Compiler. On the other hand, clock gating technique is used to save power consumption. We use PrimeTime tool (Synopsys) to estimate the power consumption. Implementation results show that the proposed architecture achieves a throughput of 853.8 Gbps at the maximum operating frequency of 667 MHz and clock gating technique allows more power savings.
Attention-DPU: Dual-path UNet with an attention mechanism for ultrasound image segmentation

Abstract With the continuous advancement in computer vision, image segmentation has achieved fruitful results in many applications such as medical image processing. In recent years, UNet and Dual Path Network (DPN) have achieved promising results in medical image segmentation. UNet cannot effectively obtain new features and reuse features. Also, DPN cannot effectively transfer the contour information of shallow blocks to the subsequent deep blocks. This paper proposes a dual path U-shaped network with the attention mechanism (Attention-DPU); taking advantage of the two networks. In the proposed network, the ordinary convolutional layer is replaced by the micro block with a dual path. Also, the attention mechanism is adopted to improve the efficiency and accuracy of segmentation.
An Ensemble of Random Decision Trees with Personalized Privacy Preservation in Edge-Cloud Computing

Edge-cloud computing (ECC) is an emerging computing paradigm, which offers a great opportunity to implement data mining-based services and applications for a large number of devices and sensors in Internet of Things. However, data privacy has been one of the most concerned problems for all the participants. In this paper, we propose a framework of privacy-preserving data mining based on private random decision trees in ECC. The framework not only gives the strong privacy guarantee, but also provides the efficient data utility. At first, we design a differential privacy-based framework to implement private random decision trees in ECC. Then, we present the concrete algorithms and the corresponding task that each participant needs to undertake. Next, we analyze the key factors to influence privacy and utility and give further improvement to increase the utility with strong privacy preservation. Finally, extensive experiments demonstrate the good performance of our designed framework.
Context-Aware Restaurant Recommendation for Group of People

Group recommendation has been widely investigated in various fields. In contrast to other research directions on group recommendations, restaurant group recommendations are more sensitive to location. However, current research has not given adequate consideration to this characteristic, so it is difficult to achieve higher global satisfaction. In this paper, we propose a context-aware restaurant group recommendation method to improve global satisfaction. We take into account the importance of location in restaurant recommendations and employ distance pre-filtering and distance-based ranking adjustment to improve recommendation satisfaction. Comprehensive experimental results show that our method can outperform other methods in terms of both global satisfaction and distance satisfaction.
Transparent Data Encryption: Comparative Analysis and Performance Evaluation of Oracle Databases

This Transparent Data Encryption (TDE) can provide enormous benefits to the Relational Databases in the aspects of Data Security, Cryptographic Encryption, and Compliances. For every transaction, the stored data must be decrypted before applying the updates as well as should be encrypted before permanently storing back at the storage level. By adding this extra functionality to the database, the general thinking denotes that the Database (DB) going to hit some performance overhead at the CPU and storage level. However, The Oracle Corporation has adversely claimed that their latest Oracle DB version 19c TDE feature can provide significant improvement in the optimization of CPU and no overhead at the storage level for data processing. Impressively, it is true. the results of this paper prove too. Most interestingly the results also revealed about highly impacted components in the servers which are not yet disclosed in any of the previous research work. This paper completely concentrates on CPU, IO, and RAM performance analysis and identifying the bottlenecks along with possible solutions.
Human Interface and Control of a Robotic Endoscope Holder Based on an AR Approach

This paper proposes an augmented reality (AR) system that facilitates surgeons to operate a robotic endoscope holder more intuitively. Through the use of an AR glasses, a surgeon can observe the endoscope view and at the same time perceive the condition of the operation instead of completely isolating from the real world, and thus also increase safety. Further, in this robotic endoscope control system, a surgeon can directly use his/her head movement to control the endoscopic view. Since the head-control approach cannot directly handle the depth of the endoscope, we propose a depth control scheme based on instrument identification. In order that the automatic depth adjustment will not interfere with the MIS operation, the system will first determine whether the surgeon is performing the operation or not. The depth control of the endoscope is allowed to work only when the surgeon is not performing the surgical instrument movement. Practical experiments using a HIWIN 6DOD robotic arm and Epson BT300 AR glasses show that the user can effectively control the movement of the endoscope to complete a tracking task of the surgical instrument.
High-density information security storage method of big data center based on fuzzy clustering

The amounts of digital data, when it is generated for each generation, valuable information called big data, have been retained. The cluster is typically used as a research technique; this practical information mining is the process. A considerable amount of diagnosis in the context of big data is established to measure the clustering processing for big data analysis. The so-called fuzzy mechanism-only framework assembled in the security storage sector may include access to the sub-iterative method. The algorithm, based on the incentive of the design and implementation of its low computational needs fuzzy clustering algorithm, big data is possible to cluster the vast data set and biased. Handle the Random Data Storing with Optimization Fuzzy Logic algorithm (RDS-FLA) proposes random data security storage and optimization be applied to the cluster data, the fuzzy logic algorithm. Some of the large-scale data set of experimental learning data has been shown. To evaluate the vague and random data security storage and the time, the attempted performance of RDS-FLA is a form of recommendation for the execution of scalability on a big data cluster. The calculations, the complexity of time and space, run the time, cluster quality, RDS-FLA is, without affecting the quality of clustering, it is about measures in the face to show that that can be performed in a short period. Therefore, the proposed algorithm, shortening the processing time, increase the efficiently stored data security. Advantages such as optimization and efficiency of such data security costs can be determined from the algorithm's experimental results.