Reviewerly/exHarmony huggingface dataset:
Applications of Blockchain Technology in Smart City Development: A Research

In 2016, India launched the smart cities mission. The objective of this mission is to developed cities by using ‘Smart’ solutions that provide core infrastructure and give citizens a decent quality of life, a clean and sustainable environment. The focus is on sustainable and inclusive development. To achieve this it uses the technology to integrate and manage the infrastructures to provide better services and ensure efficient and optimal utilisation of available resources. Incorporation of new technologies for the development of smart cities gives the new ways of rethinking different services. In this paper, we are going study the applications of blockchain technology in smart city development
A New Framework for Video Data Retrieval using Hierarchical Clustering Technique

The amount of information produced every year is rapidly growing due to many factor among all media, video is a particular media embedding visual, motion, audio and textual information. Given this huge amount of information we need general framework for video data mining to be applied to the raw videos (surveillance videos, news reading, Person reading books in library etc.).We introduce new techniques which are essential to process the video files. The first step of our frame work for mining raw video data in grouping input frames to a set of basic units which are relevant to the structure of the video. The second step is charactering the unit to cluster into similar groups, to detect interesting patterns. To do this we extract some features (object, colors etc.)From the unit. A histogram based color descriptors also introduced to reliably capture and represent the color properties of multiple images. The preliminary experimental studies indicate that the proposed framework is promising.
Filtering Method for Pre processing Mammogram Images for Breast Cancer Detection

Breast cancer is a stand-out surrounded by the most widely perceived diseases and has a high rate of mortality around the world, significantly risking the health of the females. Among existing all modalities of medical scans, mammography is the most preferred modality for preliminary examination of breast cancer. To assist radiologist, a computer-aided diagnosis (CAD) is enhancing and important medical systems for mammographic lesion analysis. CAD is necessary to provide doctors, to improve detection quality of breast cancer. In mammogram images, micro-calcifications is one of the imperative sign for breast cancer detection. Mammographic medical scan may present unwanted noise and CAD systems are very sensitive to noise. So, pre-processing of medical images for any medical image analysis application like brain tumor detection, breast cancer detection, and interstitial lung disease classification is considered as an important step. The segmentation or classification accuracy is mainly depends upon the significant improved pre-processing process. Thus, in this work, different types of filtering techniques used for noise reduction in medical image processing are analyzed. The qualitative and quantitative results are examined on mini-MIAS mammogram image database. The effectiveness of filtering techniques is compared based on the different quantitative parameters and visual qualities of examined output.
Automated Software Design Reusability using a Unique Machine Learning Technique

The era of machine learning (ML) has brought significant advancement into the traditional approaches of software development and services. Software reusability and design automation is a key requirement that can be handled through the integration of artificial intelligence (AI) capabilities with the traditional approach of software development lifecycle (SDLC) practices. The study introduces a novel approach of ML, which can assist inappropriate selection of reusable software components, which in the long run, can optimize the operational cost in the context of development practices and also speed up the service delivery performance of software engineering activities. The proposed model is validated through a numerical analysis that shows the effectiveness of the system in terms of both classification accuracy and computational efficiency.
3D Printed Metamaterial Capacitive Sensing Array for Universal Jamming Gripper and Human Joint Wearables

The metamaterial sensor array has the ability to conform to a spherical surface as shown. The slight separation between the array and the sphere implies that the array is not only limited to a spherical surface but can conform to other curved surfaces. A blown-up view shows capacitive sensor electrodes being embedded within the metamaterial sensor array. Further details can be found in the article, number 2001082, by Jian Zhu, Wen Feng Lu, and co-workers.
Analysis of Customer Behavior in Mobile Food Ordering Application Using UTAUT Model (Case Study: GoFood Application)

One of the mobile application technologies that popular and enjoying extraordinary success in Indonesia is GoJek application. One of many services in gojek app is called go-food. Go-food provides food online service delivery that has three main users. There are merchants, gojek drivers, and customers. Many online food ordering applications have developed, and various issues have occurred, but there has not been much research on the topic. This research is using quantitative methods. Tools to get data is a questionnaire of 60 respondents. The results are validation data and structural model calculation use Smart PLS tools. From the measurement results, there are two variables that have a positive and significant effect (with a t-statistic score&gt; 1.65) on the user's intention to use GoFood, namely the Social Influence and Perceived Control variables. There also performance expectancy, effort expectancy, and facilitating condition variables that have a negative effect (with a t-statistic score &lt;1.65) on the user's intention to use the GoFood application.
Mobile Robot Global Path Planning Based on Improved Ant Colony System Algorithm with Potential Field

摘要: 针对势场蚁群算法路径转折点数量过多、收敛速度过快、容易陷入局部最优等问题,提出了基于势场跳点的蚁群算法。该算法融合了蚁群算法和跳点搜索算法的搜索策略,使规划出的路径更加平滑;引入了势场合力递减系数,减少了势场蚁群算法因势场而陷入的局部最优问题;引入了简化的跳点搜索算法对初始化信息素进行更新,提高了算法前期的搜索效率。为验证该算法的有效性,使用不同规格的栅格地图进行了仿真试验,仿真结果表明,相比于势场蚁群算法,该算法能够有效减少收敛迭代次数,其收敛搜索时间更短,且最终搜索到的路径更优。最后将该算法应用到实际的基于ROS的移动机器人导航试验中,试验结果表明,该算法能有效解决移动机器人全局路径规划问题,且能明显提升机器人全局路径规划的效率。
An interpretable and versatile machine learning approach for oocyte phenotyping

Meiotic maturation is a crucial step of oocyte formation allowing its potential fertilization and embryo development. Elucidating this process is important both for fundamental research and assisted reproductive technology. Few computational tools based on non-invasive measurements are however available to characterize oocyte meiotic maturation. Here, we develop a computational framework to phenotype oocytes based on images acquired in transmitted light. We trained neural networks to segment the contour of oocytes and their zona pellucida using oocytes from diverse species. We defined a comprehensive set of morphological features to describe an oocyte. These steps are implemented in an open-source Fiji plugin. We present a feature based machine learning pipeline to recognize oocyte populations and determine their morphological differences. We first demonstrate its potential to screen oocyte from different strains and automatically identify their morphological characteristics. Its second application is to predict and characterize the maturation potential of oocytes. We identify the texture of the zona pellucida and the cytoplasmic particles size as features to assess mouse oocyte maturation potential and tested whether these features were applicable to human oocyte's developmental potential.
Unsupervised Sentence Representation via Contrastive Learning with Mixing Negatives

Unsupervised sentence representation learning is a fundamental problem in natural language processing. Recently, contrastive learning has made great success on this task. Existing constrastive learning based models usually apply random sampling to select negative examples for training. Previous work in computer vision has shown that hard negative examples help contrastive learning to achieve faster convergency and better optimization for representation learning. However, the importance of hard negatives in contrastive learning for sentence representation is yet to be explored. In this study, we prove that hard negatives are essential for maintaining strong gradient signals in the training process while random sampling negative examples is ineffective for sentence representation. Accordingly, we present a contrastive model, MixCSE, that extends the current state-of-the-art SimCSE by continually constructing hard negatives via mixing both positive and negative features. The superior performance of the proposed approach is demonstrated via empirical studies on Semantic Textual Similarity datasets and Transfer task datasets.
Classifying the lifestyle status for Alzheimer’s disease from clinical notes using deep learning with weak supervision

Abstract Background Since no effective therapies exist for Alzheimer’s disease (AD), prevention has become more critical through lifestyle status changes and interventions. Analyzing electronic health records (EHRs) of patients with AD can help us better understand lifestyle’s effect on AD. However, lifestyle information is typically stored in clinical narratives. Thus, the objective of the study was to compare different natural language processing (NLP) models on classifying the lifestyle statuses (e.g., physical activity and excessive diet) from clinical texts in English. Methods Based on the collected concept unique identifiers (CUIs) associated with the lifestyle status, we extracted all related EHRs for patients with AD from the Clinical Data Repository (CDR) of the University of Minnesota (UMN). We automatically generated labels for the training data by using a rule-based NLP algorithm. We conducted weak supervision for pre-trained Bidirectional Encoder Representations from Transformers (BERT) models and three traditional machine learning models as baseline models on the weakly labeled training corpus. These models include the BERT base model, PubMedBERT (abstracts + full text), PubMedBERT (only abstracts), Unified Medical Language System (UMLS) BERT, Bio BERT, Bio-clinical BERT, logistic regression, support vector machine, and random forest. The rule-based model used for weak supervision was tested on the GSC for comparison. We performed two case studies: physical activity and excessive diet, in order to validate the effectiveness of BERT models in classifying lifestyle status for all models were evaluated and compared on the developed Gold Standard Corpus (GSC) on the two case studies. Results The UMLS BERT model achieved the best performance for classifying status of physical activity, with its precision, recall, and F-1 scores of 0.93, 0.93, and 0.92, respectively. Regarding classifying excessive diet, the Bio-clinical BERT model showed the best performance with precision, recall, and F-1 scores of 0.93, 0.93, and 0.93, respectively. Conclusion The proposed approach leveraging weak supervision could significantly increase the sample size, which is required for training the deep learning models. By comparing with the traditional machine learning models, the study also demonstrates the high performance of BERT models for classifying lifestyle status for Alzheimer’s disease in clinical notes.
Design and Experience of Mobile Applications: A Pilot Survey

With the tremendous growth in mobile phones, mobile application development is an important emerging arena. Moreover, various applications fail to serve the purpose of getting the attention of the intended users, which is determined by their User Interface (UI) and User Experience (UX). As a result, developers often find it challenging to meet the users’ expectations. To date, several reviews have been carried out which explored various aspects of design and the experience of mobile applications using UX/UI. However, many of these existing surveys primarily focused on only some of the issues in isolation but did not consider all the major parameters such as visualisation/graphics, context, user behaviour/emotions/control, usability, adaptability/flexibility, language, and feedback. In our pilot survey, we gathered the preferences and perceptions of a heterogeneous group of concerned people and considered all the aforementioned parameters. These preferences would serve as a reference to mobile application developers, giving them useful insights. Our proposed approach would help mobile application developers and designers focus on the particular UI/UX problems of mobile applications as per their relevant context. A comparative analysis of the various UI and UX factors that determine a mobile application interface is presented in this paper.
Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis

Cross-domain sentiment analysis has achieved promising results with the help of pre-trained language models.As GPT-3 appears, prompt tuning has been widely explored to enable better semantic modeling in many natural language processing tasks.However, directly using a fixed predefined template for crossdomain research cannot model different distributions of the [MASK] token in different domains, thus making underuse of the prompt tuning technique.In this paper, we propose a novel Adversarial Soft Prompt Tuning method (AdSPT) to better model cross-domain sentiment analysis.On the one hand, AdSPT adopts separate soft prompts instead of hard templates to learn different vectors for different domains, thus alleviating the domain discrepancy of the [MASK] token in the masked language modeling task.On the other hand, AdSPT uses a novel domain adversarial training strategy to learn domain-invariant representations between each source domain and the target domain.Experiments on a publicly available sentiment analysis dataset show that our model achieves new state-of-the-art results for both single-source domain adaptation and multi-source domain adaptation.
Dynamic graph Conv-LSTM model with dynamic positional encoding for the large-scale traveling salesman problem

&lt;abstract&gt;&lt;p&gt;Recent research has showen that deep reinforcement learning (DRL) can be used to design better heuristics for the traveling salesman problem (TSP) on the small scale, but does not do well when generalized to large instances. In order to improve the generalization ability of the model when the nodes change from small to large, we propose a dynamic graph Conv-LSTM model (DGCM) to the solve large-scale TSP. The noted feature of our model is the use of a dynamic encoder-decoder architecture and a convolution long short-term memory network, which enable the model to capture the topological structure of the graph dynamically, as well as the potential relationships between nodes. In addition, we propose a dynamic positional encoding layer in the DGCM, which can improve the quality of solutions by providing more location information. The experimental results show that the performance of the DGCM on the large-scale TSP surpasses the state-of-the-art DRL-based methods and yields good performance when generalized to real-world datasets. Moreover, our model compares favorably to heuristic algorithms and professional solvers in terms of computational time.&lt;/p&gt;&lt;/abstract&gt;
D2FE-GAN: Decoupled dual feature extraction based GAN for MRI image synthesis

Magnetic resonance imaging (MRI) technique can generate various tissue contrasts by using different pulse sequences and parameters. However, obtaining multiple different contrast images for the same patient is sometimes time-consuming and costly. In this paper, we propose a novel generative adversarial network based on decoupled dual feature representations (D2FE-GAN) for cross-modality MRI synthesis. Inspired by the previous works of image style transferring, we argue that the MRI images can be viewed as a compound of underlying information shared among the bodies of modalities (e.g., semantic information), and representative information varying with the styles of modalities (e.g., edges, contrasts). Different from the existing GAN-based methods that pay attention to either the body consistency or the style refinement, the proposed D2FE-GAN method considers both aspects for better synthesis. Specifically, our method decouples the underlying information and the representative information from the source modality and target modality, respectively, through two dissimilar encoders. In response to the invisibility of target modality in testing phase, we propose to employ a Residual Network firstly to generate an intermediate modality as the pseudo target modality. Subsequently, the decoupled two kinds of information will be integrated through a decoder. Here, we introduce the Adaptive Instance Normalization layer, in which the affine parameters are replaced by the mean and standard deviation of the representative information, thus completing the fusion processing of feature space information. Experimental results on BRATS2015 dataset and IXI dataset show that the proposed method outperforms the state-of-the-art image synthesis approaches in both qualitative and quantitative measures.
Generation of a Maximally Entangled State Using Collective Optical Pumping

We propose and implement a novel scheme for dissipatively pumping two qubits into a singlet Bell state. The method relies on a process of collective optical pumping to an excited level, to which all states apart from the singlet are coupled. We apply the method to deterministically entangle two trapped ^{40}Ca^{+} ions. Within 16 pumping cycles, an initially separable state is transformed into one with 83(1)% singlet fidelity, and states with initial fidelity of ⪆70% converge onto a fidelity of 93(1)%. We theoretically analyze the performance and error susceptibility of the scheme and find it to be insensitive to a large class of experimentally relevant noise sources.
R-YOLO: A YOLO-Based Method for Arbitrary-Oriented Target Detection in High-Resolution Remote Sensing Images

In view of the existence of remote sensing images with large variations in spatial resolution, small and dense objects, and the inability to determine the direction of motion, all these components make object detection from remote sensing images very challenging. In this paper, we propose a single-stage detection network based on YOLOv5. This method introduces the MS Transformer module at the end of the feature extraction network of the original network to enhance the feature extraction capability of the network model and integrates the Convolutional Block Attention Model (CBAM) to find the attention area in dense scenes. In addition, the YOLOv5 target detection network is improved by incorporating a rotation angle approach from the a priori frame design and the bounding box regression formulation to make it suitable for rotating frame-based detection scenarios. Finally, the weighted combination of the two difficult sample mining methods is used to improve the focal loss function, so as to improve the detection accuracy. The average accuracy of the test results of the improved algorithm on the DOTA data set is 77.01%, which is higher than the previous detection algorithm. Compared with the average detection accuracy of YOLOv5, the average detection accuracy is improved by 8.83%. The experimental results show that the algorithm has higher detection accuracy than other algorithms in remote sensing scenes.
On Tackling Explanation Redundancy in Decision Trees

Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models. The interpretability of decision trees motivates explainability approaches by so-called intrinsic interpretability, and it is at the core of recent proposals for applying interpretable ML models in high-risk applications. The belief in DT interpretability is justified by the fact that explanations for DT predictions are generally expected to be succinct. Indeed, in the case of DTs, explanations correspond to DT paths. Since decision trees are ideally shallow, and so paths contain far fewer features than the total number of features, explanations in DTs are expected to be succinct, and hence interpretable. This paper offers both theoretical and experimental arguments demonstrating that, as long as interpretability of decision trees equates with succinctness of explanations, then decision trees ought not be deemed interpretable. The paper introduces logically rigorous path explanations and path explanation redundancy, and proves that there exist functions for which decision trees must exhibit paths with explanation redundancy that is arbitrarily larger than the actual path explanation. The paper also proves that only a very restricted class of functions can be represented with DTs that exhibit no explanation redundancy. In addition, the paper includes experimental results substantiating that path explanation redundancy is observed ubiquitously in decision trees, including those obtained using different tree learning algorithms, but also in a wide range of publicly available decision trees. The paper also proposes polynomial-time algorithms for eliminating path explanation redundancy, which in practice require negligible time to compute. Thus, these algorithms serve to indirectly attain irreducible, and so succinct, explanations for decision trees. Furthermore, the paper includes novel results related with duality and enumeration of explanations, based on using SAT solvers as witness-producing NP-oracles.
Middle-Level Feature Fusion for Lightweight RGB-D Salient Object Detection

Most existing RGB-D salient object detection (SOD) models adopt a two-stream structure to extract the information from the input RGB and depth images. Since they use two subnetworks for unimodal feature extraction and multiple multi-modal feature fusion modules for extracting cross-modal complementary information, these models require a huge number of parameters, thus hindering their real-life applications. To remedy this situation, we propose a novel middle-level feature fusion structure that allows to design a lightweight RGB-D SOD model. Specifically, the proposed structure first employs two shallow subnetworks to extract low- and middle-level unimodal RGB and depth features, respectively. Afterward, instead of integrating middle-level unimodal features multiple times at different layers, we just fuse them once via a specially designed fusion module. On top of that, high-level multi-modal semantic features are further extracted for final salient object detection via an additional subnetwork. This will greatly reduce the network's parameters. Moreover, to compensate for the performance loss due to parameter deduction, a relation-aware multi-modal feature fusion module is specially designed to effectively capture the cross-modal complementary information during the fusion of middle-level multi-modal features. By enabling the feature-level and decision-level information to interact, we maximize the usage of the fused cross-modal middle-level features and the extracted cross-modal high-level features for saliency prediction. Experimental results on several benchmark datasets verify the effectiveness and superiority of the proposed method over some state-of-the-art methods. Remarkably, our proposed model has only 3.9M parameters and runs at 33 FPS.