Reviewerly/exHarmony huggingface dataset:
Research on classification diagnosis model of psoriasis based on deep residual network

A classification and diagnosis model for psoriasis based on deep residual network is proposed in this paper. Which using deep learning technology to classify and diagnose psoriasis can help reduce the burden of doctors, simplify the diagnosis and treatment process, and improve the quality of diagnosis. Firstly, data enhancement, image resizings, and TFRecord coding are used to preprocess the input of the model, and then a 34-layer deep residual network (ResNet-34) is constructed to extract the characteristics of psoriasis. Finally, we used the Adam algorithm as the optimizer to train ResNet-34, used cross-entropy as the loss function of ResNet-34 in this study to measure the accuracy of the model, and obtained an optimized ResNet-34 model for psoriasis diagnosis. The experimental results based on k-fold cross validation show that the proposed model is superior to other diagnostic methods in terms of recall rate, F1-score and ROC curve. The ResNet-34 model can achieve accurate diagnosis of psoriasis, and provide technical support for data analysis and intelligent diagnosis and treatment of psoriasis.
Bangla Sign alphabet recognition with zero-shot and transfer learning

Bangla, being the fifth most spoken language in the world has its own distinct sign language with two methods (one-handed and two-handed) of representation. However, a standard automatic recognition system of Bangla sign language (BdSL) is still to be achieved. Though widely studied and explored by researchers in the past years, certain unaddressed issues like identifying unseen signs and both types of BdSL or lack of evaluation of the models in versatile environmental conditions demarcate the real-world implementation of the automatic recognition of BdSL. To find a probable solution to the shortcomings in the existing works, this paper proposes two approaches based on conventional transfer learning and contemporary Zero-shot learning (ZSL) for automatic BdSL alphabet recognition of both seen and unseen data. The performance of the proposed system is evaluated for both types of Bangla sign representations as well as on a large dataset with 35,149 images from over 350 subjects, varying in terms of backgrounds, camera angle, light contrast, skin tone, hand size, and orientation. For the ZSL approach, a new semantic descriptor dedicated to BdSL is created and a split of the dataset into seen and unseen classes is proposed. Our model achieved 68.21%, 91.57%, and 54.34% of harmonic mean accuracy, seen accuracy, and zero-shot accuracy with six unseen classes respectively. For the transfer learning-based approach, we found pre-trained DenseNet201 architecture to be the best performing feature extractor and Linear Discriminant Analysis as the best classifier with an overall accuracy of 93.68% on the large dataset after conducting quantitative experimentation on 18 CNN architectures and 21 classifiers. The satisfactory result from our models supports its very probative potential to serve extensively for the hearing and speaking impaired community.
Hand Sign to Bangla Speech: A Deep Learning in Vision Based System for Recognizing Hand Sign Digits and Generating Bangla Speech

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MSGSE-Net: Multi-scale guided squeeze-and-excitation network for subcortical brain structure segmentation

Convolutional neural networks (CNNs) have been achieving remarkable results in medical image segmentation. However, for accurate segmentation of subcortical brain structure in MR images, it is still a challenge due to the ambiguous boundaries, the complex structures, and the various shapes, which limits their clinical application. In this paper, we focus on utilizing multi-scale image contexts and attention mechanisms to improve networks’ ability to learn discriminative feature representation for accurate segmentation and present a novel FCNN architecture called multis-scale guided squeeze-and-excitation network (MSGSE-Net). In particular, we first propose the multi-scale guided squeeze-and-excitation (MSGSE) attention module which can progressively and selectively aggregate discriminative features. In contrast to existing attention modules, the MSGSE module performs an adaptive recalibration that features at different locations of the feature map are recalibrated under the guidance of multi-scale contexts. Then multi-scale spatial attention supervision is adopted to enhance the intra-class homogeneity and inter-class distinction of the attention weights. Moreover, we propose a novel entropy-weighted Dice loss (EDL) to force the network to focus on the ambiguous voxels around the boundaries of subcortical structures. We evaluate the proposed method on two challenging benchmark datasets (the IBSR dataset and the MALC dataset). The experimental results show that our model consistently yields better segmentation performance than several state-of-the-art methods and improves the segmentation Dice score by 1.6% at most compared with baseline method U-Net. Our code is available at https://github.com/neulxlx/MSGSE-Net.
Parallel and Asynchronous Smart Contract Execution

Today's blockchains suffer from low throughput and high latency, which impedes their widespread adoption of more complex applications like smart contracts. In this article, we propose a novel paradigm for smart contract execution. It distinguishes between consensus nodes and execution nodes: different groups of execution nodes can execute transactions in parallel; meanwhile, consensus nodes can asynchronously order transactions and process execution results. Moreover, it requires no coordination among execution nodes and can effectively prevent livelocks. We show two ways of applying this paradigm to blockchains. First, we show how we can make Ethereum support parallel and asynchronous contract execution <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without hard-forks</i> . Then, we propose a new public, permissionless blockchain. Our benchmark shows that, with a fast consensus layer, it can provide a high throughput even for complex transactions like Cryptokitties gene mixing. It can also protect simple transactions from being starved by complex transactions.
Learning graph edit distance by graph neural networks

The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies.In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance.Hence, we propose an efficient graph distance based on the novel field of geometric deep learning.Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation.The performance of the proposed graph distance is validated on two different scenarios.On the one hand, in a graph retrieval of handwritten words i.e. keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks.On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset.
Contextualized query expansion via unsupervised chunk selection for text retrieval

When ranking a list of documents relative to a given query, the vocabulary mismatches could compromise the performance, as a result of the different language used in the queries and the documents. Though the BERT-based re-ranker have significantly advanced the state-of-the-art, such mismatch still exist. Moreover, recent works demonstrated that it is non-trivial to use the established query expansion methods to boost the performance of BERT-based re-rankers. Henceforth, this paper proposes a novel query expansion model using unsupervised chunk selection, coined as BERT-QE. In particular, BERT-QE consists of three phases. After performing the first-round re-ranking in phase one, BERT-QE leverages the strength of the BERT model to select relevant text chunks from feedback documents in phase two and uses them for the final re-ranking in phase three. Furthermore, different variants of BERT-QE are thoroughly investigated for a better trade-off between effectiveness and efficiency, including the uses of smaller BERT variants and of recently proposed late interaction methods. On the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models. Actually, the best variant of BERT-QE can outperform BERT-Large significantly on shallow metrics with less than 1% extra computations.
SmartScan: An approach to detect Denial of Service Vulnerability in Ethereum Smart Contracts

Blockchain technology's (BT) Ethereum Smart Contracts allows programmable transactions that involve the transfer of monetary assets among peers on a BT network independent of a central authorizing agency. Ethereum Smart Contracts are programs that are deployed as decentralized applications, having the building blocks of the blockchain consensus protocol. This technology enables consumers to make agreements in a transparent and conflict-free environment. However, the security vulnerabilities within these smart contracts are a potential threat to the applications and their consumers and have shown in the past to cause huge financial losses. In this paper, we propose a framework that combines static and dynamic analysis to detect Denial of Service (DoS) vulnerability due to an unexpected revert in Ethereum Smart Contracts. Our framework, SmartScan, statically scans smart contracts under test (SCUTs) to identify patterns that are potentially vulnerable in these SCUTs and then uses dynamic analysis to precisely confirm their exploitability of the DoS-Unexpected Revert vulnerability, thus achieving increased performance and more precise results. We evaluated SmartScan on a set of 500 smart contracts collected from the Etherscan. Our approach shows an improvement in precision and recall when compared to available state of the art techniques.
An adaptive outlier removal aided k-means clustering algorithm

K-means is one of ten popular clustering algorithms. However, k-means performs poorly due to the presence of outliers in real datasets. Besides, a different distance metric makes a variation in data clustering accuracy. Improve the clustering accuracy of k-means is still an active topic among researchers of the data clustering community from outliers removal and distance metrics perspectives. Herein, a novel modification of the k-means algorithm is proposed based on Tukey’s rule in conjunction with a new distance metric. The standard Tukey rule is modified to remove the outliers adaptively by considering whether the data is distributed to the left, right or even to the input data's mean value. The elimination of outliers is applied in the proposed modification of the k-means before calculating the centroids to minimize the outliers' influences. Meanwhile, a new distance metric is proposed to assign each data point to the nearest cluster. In this research, the modified k-means significantly improves the clustering accuracy and centroids convergence. Moreover, the proposed distance metric's overall performance outperforms most of the literature distance metrics. This manuscript's presented work demonstrates the significance of the proposed technique to improve the overall clustering accuracy up to 80.57% on nine standard multivariate datasets.
Make complex CAPTCHAs simple: A fast text captcha solver based on a small number of samples

Text-based captchas are still widely used by many websites such as Wikipedia and Microsoft despite the emergence of many alternative captchas. Recently, the design of text-based captchas has become more and more complex to resist attacks from automatic cracking programs. However, most of the existing captcha solving methods have certain shortcomings, such as insufficient accuracy, poor generalization performance, and the need for a large number of labeled samples. This study proposes a fast captcha solver that can effectively break text-based captchas with complex security features using a small amount of labeled data. The solver was achieved by constructing a captcha transformation model based on generative adversarial networks to simplify the captcha images before character segmentation and recognition. Results showed that the proposed captcha solver achieved a high success rate of over 96% character accuracy and 74% captcha accuracy for all evaluated schemes. Moreover, the average time to process a single captcha image using a laptop GPU was only 4–8 ms. The effectiveness of this work may encourage captcha designers to reconsider a more secure human–machine distinction mechanism.
Optimizing Blockchain Based Smart Grid Auctions: A Green Revolution

Traditional smart grid energy auctions cannot directly be integrated in blockchain due to its decentralized nature. Therefore, research works are being carried out to propose efficient decentralized auctions for energy trading. Since, blockchain is a novel paradigm which ensures trust, but it also comes up with a curse of high computation and communication complexity which eventually causes resource scarcity, therefore, there is a need to develop and encourage development of greener and computational-friendly auctions to carry out decentralized energy trading. In this paper, we first provide a thorough motivation of decentralized auctions over traditional auctions. Afterwards, we provide in-depth design requirements that can be taken into consideration while developing such auctions. After that, we analyse technical works that have developed blockchain based energy auctions from green perspective. Finally, we summarize the article by providing challenges and possible future research directions of blockchain based energy auction from green viewpoint.
A Practical and Efficient Bidirectional Access Control Scheme for Cloud-Edge Data Sharing

The cloud computing paradigm provides numerous tempting advantages, enabling users to store and share their data conveniently. However, users are naturally resistant to directly outsourcing their data to the cloud since the data often contain sensitive information. Although several fine-grained access control schemes for cloud-data sharing have been proposed, most of them focus on the access control of the encrypted data (e.g., restricting the decryption capabilities of the receivers). Distinct from the existing work, this article aims to address this challenging problem by developing a more practical bidirectional fine-grained access control scheme that can restrict the capabilities of both senders and receivers. To this end, we systematically investigate the access control for cloud data sharing. Inspired by the access control encryption (ACE), we propose a novel data sharing framework that combines the cloud side and the edge side. The edge server is located in the middle of all the communications, checking and preventing illegal communications according to the predefined access policy. Next, we develop an efficient access control algorithm by exploiting the attribute-based encryption and proxy re-encryption for the proposed framework. The experimental results show that our scheme exhibits superior performance in the encryption and decryption compared to the prior work.
Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques

Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.
YogaHelp: Leveraging Motion Sensors for Learning Correct Execution of Yoga With Feedback

By leveraging motion sensors, the study of physical activities has become effortless and convenient. Yoga is an excellent form of physical activity or exercise that involves all parts of the body. Regular practice of yoga improves both the physical and mental health of the people, if the practitioner performs it correctly. Though the existing exercise recognition methods have shown quite a good performance using deep neural networks, they fail to assess the correctness of execution. Feedback describing correctness level is very useful to an amateur practitioner. In this article, we aim to develop a YogaHelp system to help amateurs for learning the correct execution of yoga without any supervision of a trainer. The importance of such a system is much higher during a pandemic situation, where the trainers can not be availed due to the risk of catching an infection. YogaHelp leverages the motion sensors including accelerometer and gyroscope, to recognize 12 linked-steps of sun salutation (Surya Namaskar) yoga along with their correctness level. YogaHelp employs a deep learning model built using convolutional layers for yoga step recognition without explicit feature extraction. The novelty of the system lies in the feedback that describes the speed of execution and angular deviation of the posture from the standard ones. We develop a prototype for collecting a training dataset from eight professional yoga trainers and for validating the effectiveness of the system.
Web-based chatbot for Frequently Asked Queries (FAQ) in Hospitals

Local hospitals are operated by the resigned association of patients as passive communication channels. The online hospital data related to the users' queries are not transparent and reliable. Therefore, it is crucial to have an intelligent web chatbot that manages user requests and provides quick access to local hospital information. In this paper, we present a framework and functionality of a chatbot developed using web technologies.The bot engine was integrated by several machine learning approaches like gradient descent (GD) and natural language processing (NLP) algorithms. The trained data entered into the bot were split into mini-word batches, and the GD algorithm was applied sequentially on each mini-batch. The NLP methods involved in converting a word to its stem with a text result less readable by humans.The employed ML algorithms were successfully incorporated to manage the alternative synchronisation of text and voice messages.The proposed bot can be a better solution for data extraction from local hospital which functioning as a good communication channel for both users and hospital staff and helpful in reducing the crowd.تدار المستشفيات المحلية من قبل جمعية المرضى المستقيلة كقنوات اتصال سلبية. بيانات المستشفى عبر الإنترنت المتعلقة باستفسارات المستخدمين ليست مضمونة من حيث الشفافية والموثوقية. لذلك، من الضروري أن يكون لديك روبوت محادثة ذكي على الويب يمكنه استكشاف إدارة طلبات المستخدم، وتوفير وصول سريع لمعلومات المستشفى المحلي. في هذه الورقة، قدم المؤلفون إطار عمل ووظيفة روبوت محادثة تم تطويره بواسطة خبراء في تقنيات الويب.تم دمج محرك الروبوت من خلال العديد من مناهج التعلم الآلي مثل خوارزميات الانحدار ومعالجة اللغة الطبيعية. وتم تقسيم البيانات المدربة التي تم إدخالها في الروبوت إلى مجموعات من الكلمات المصغرة وتم تطبيق خوارزمية الانحدار على كل دفعة صغيرة بالتتابع. طرق البرمجة اللغوية العصبية المتضمنة في تحويل الكلمة إلى جذعها مع نتيجة نصية منخفضة يمكن قراءتها من قبل البشر.تم دمج الخوارزميات المستخدمة بنجاح لإدارة المزامنة البديلة للرسائل النصية والصوتية.يمكن أن يكون الروبوت المقترح حلاً أفضل لاستخراج البيانات من المستشفيات المحلية. إنها قناة اتصال ثاقبة لكل من المستخدمين وموظفي المستشفى.
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics

Neural network ensembles are gaining popularity by harnessing the complementary wisdom of multiple base models. Ensemble teams with high diversity promote high failure independence, which is effective for boosting the overall ensemble accuracy. This paper provides an in-depth study on how to design and compute ensemble diversity, which can capture the complementary decision capacity of ensemble member models. We make three original contri-butions. First, we revisit the ensemble diversity metrics in the literature and analyze the inherent problems of poor correlation between ensemble diversity and ensemble ac-curacy, which leads to the low quality ensemble selection using such diversity metrics. Second, instead of computing diversity scores for ensemble teams of different sizes using the same criteria, we introduce focal model based ensemble diversity metrics, coined as FQ-diversity metrics. Our new metrics significantly improve the intrinsic correlation between high ensemble diversity and high ensemble accuracy. Third, we introduce a diversity fusion method, coined as the EQ-diversity metric, by integrating the top three most representative FQ-diversity metrics. Comprehensive experiments on two benchmark datasets (CIFAR-10 and ImageNet) show that our FQ and EQ diversity metrics are effective for selecting high diversity ensemble teams to boost overall ensemble accuracy.
Quantum Annealing versus Digital Computing

Quantum annealing is getting increasing attention in combinatorial optimization. The quantum processing unit by D-Wave is constructed to approximately solve Ising models on so-called Chimera graphs. Ising models are equivalent to quadratic unconstrained binary optimization (QUBO) problems and maximum cut problems on the associated graphs. We have tailored branch-and-cut as well as semidefinite programming algorithms for solving Ising models for Chimera graphs to provable optimality and use the strength of these approaches for comparing our solution values to those obtained on the current quantum annealing machine, D-Wave 2000Q. This allows for the assessment of the quality of solutions produced by the D-Wave hardware. In addition, we also evaluate the performance of a heuristic by Selby. It has been a matter of discussion in the literature how well the D-Wave hardware performs at its native task, and our experiments shed some more light on this issue. In particular, we examine how reliably the D-Wave computer can deliver true optimum solutions and present some surprising results.
Illuminating the space of beatable lode runner levels produced by various generative adversarial networks

Generative Adversarial Networks (GANs) are capable of generating convincing imitations of elements from a training set, but the distribution of elements in the training set affects the difficulty of properly training the GAN and the quality of the outputs it produces. This paper looks at six different GANs trained on different subsets of data from the game Lode Runner. The quality diversity algorithm MAP-Elites was used to explore the set of quality levels that could be produced by each GAN, where quality was defined as being beatable and having the longest solution path possible. Interestingly, a GAN trained on only 20 levels generated the largest set of diverse beatable levels while a GAN trained on 150 levels generated the smallest set of diverse beatable levels, thus challenging the notion that more data is always better when training GANs.