Reviewerly/exHarmony huggingface dataset:
Method of Constructing a Nonlinear Approximating Scheme of a Complex Signal: Application Pattern Recognition

A method for identification of structures of a complex signal and noise suppression based on nonlinear approximating schemes is proposed. When we do not know the probability distribution of a signal, the problem of identifying its structures can be solved by constructing adaptive approximating schemes in an orthonormal basis. The mapping is constructed by applying threshold functions, the parameters of which for noisy data are estimated to minimize the risk. In the absence of a priori information about the useful signal and the presence of a high noise level, the use of the optimal threshold is ineffective. The paper introduces an adaptive threshold, which is assessed on the basis of the posterior risk. Application of the method to natural data has confirmed its effectiveness.
Efficient Neural Architecture Search for End-to-End Speech Recognition Via Straight-Through Gradients

Neural Architecture Search (NAS), the process of automating architecture engineering, is an appealing next step to advancing end-to-end Automatic Speech Recognition (ASR), replacing expert-designed networks with learned, task-specific architectures. In contrast to early computational-demanding NAS methods, recent gradient-based NAS methods, e.g., DARTS (Differentiable ARchiTecture Search), SNAS (Stochastic NAS) and ProxylessNAS, significantly improve the NAS efficiency. In this paper, we make two contributions. First, we rigorously develop an efficient NAS method via Straight-Through (ST) gradients, called ST-NAS. Basically, ST-NAS uses the loss from SNAS but uses ST to back-propagate gradients through discrete variables to optimize the loss, which is not revealed in ProxylessNAS. Using ST gradients to support sub-graph sampling is a core element to achieve efficient NAS beyond DARTS and SNAS. Second, we successfully apply ST-NAS to end-to-end ASR. Experiments over the widely benchmarked 80-hour WSJ and 300-hour Switchboard datasets show that the ST-NAS induced architectures significantly outperform the human-designed architecture across the two datasets. Strengths of ST-NAS such as architecture transferability and low computation cost in memory and time are also reported.
Progressive face super-resolution with cascaded recurrent convolutional network

Progressive upsampling is beneficial for deep learning based large factor (e.g., 8×) super-resolution (SR) to improve network performance and reduce the difficulties of network training. The feedback mechanism is helpful in strengthening the representation power of deep networks since it can efficiently enlarge the receptive field. In this paper, we propose a progressive cascaded recurrent convolutional network, dubbed PCRCN, for large factor face SR (FSR). Specifically, a novel multi-stage cascaded convolutional neural network is developed to progressively obtain high magnification face images, where the first stage of network achieves an initial 2× magnification image, and the following other stages, adopting the recurrent structure, sequentially generate the corresponding 4×,8× and possibly larger factor SR images through multiple independent iterative modules. The deep features and parsing priors of face are extracted in parallel in each stage of network, and integrated to improve the deep representation ability of network. The training of the whole network is supervised in an end-to-end way by the weighted sum of multiple losses. Compared with other state-of-the-art methods, the experimental results show that the proposed method can achieve superior results in terms of both subjective and objective evaluations.
Jooka: A Bilingual Chatbot for University Admission

High school students start to apply to universities during their senior year. This marks the beginning of a frustrating and stressful period for the students as well as the admission staff. Students have countless queries and issues that need to be repeatedly answered and solved by the admission staff. However, with the rise of modern technologies such as conversational agents, the admission process can be automated to decrease the load of the admission staff. Therefore, we investigated the usefulness of a chatbot for university admission that is designed for a specific target demographic. A chatbot was developed named "Jooka" for a university to improve their admission process. Since our target audience is predominately bilingual, we added bilingualism support to Jooka. Jooka understands queries written in English and Arabic, and responds according to the query language. A survey was conducted to evaluate the performance of Jooka with parents and high school participants. Overall, the results showed great acceptance and willingness from users to adopt the new technology.
Confidence Calibration for Domain Generalization under Covariate Shift

Existing calibration algorithms address the problem of covariate shift via unsupervised domain adaptation. However, these methods suffer from the following limitations: 1) they require unlabeled data from the target domain, which may not be available at the stage of calibration in real-world applications and 2) their performance depends heavily on the disparity between the distributions of the source and target domains. To address these two limitations, we present novel calibration solutions via domain generalization. Our core idea is to leverage multiple calibration domains to reduce the effective distribution disparity between the target and calibration domains for improved calibration transfer without needing any data from the target domain. We provide theoretical justification and empirical experimental results to demonstrate the effectiveness of our proposed algorithms. Compared against state-of-the-art calibration methods designed for domain adaptation, we observe a decrease of 8.86 percentage points in expected calibration error or, equivalently, an increase of 35 percentage points in improvement ratio for multi-class classification on the Office-Home dataset.
Detecting the target of sarcasm is hard: Really??

Sarcasm target detection (identifying the target of mockery in a sarcastic sentence) is an emerging field in computational linguistics. Although there has been some research in this field, accurately identifying the target still remains problematic especially when the target of mockery is not presented in the text. In this paper, we propose a combination of a machine learning classifier and a deep learning model to extract the target of sarcasm from the text. First, we classify sarcastic sentences using machine learning, to determine whether a sarcastic sentence contains a target. Then we use a deep learning model from Aspect-Based Sentiment Analysis to extract the target. Our proposed system is evaluated on three publicly available data sets: sarcastic book snippets, sarcastic tweets, and sarcastic Reddit comments. Our evaluation results show that our approach achieves equal or better performance compared to the current state-of-the-art system, with an 18% improvement on the Reddit data set and similar scores on the Books and Tweets data sets. This is because our method is able to accurately identify when the target of sarcasm is not present. The primary challenge we identify, that is hindering the creation of a high accuracy classifier, is the lack of consistency among human annotators in identifying the target of sarcasm within standard ground-truth data sets.
Internet of Things-enabled Passive Contact Tracing in Smart Cities

Contact tracing has been proven an essential practice during pandemic outbreaks and is a critical non-pharmaceutical intervention to reduce mortality rates. While traditional contact tracing approaches are gradually being replaced by peer-to-peer smartphone-based systems, the new applications tend to ignore the Internet-of-Things (IoT) ecosystem that is steadily growing in smart city environments. This work presents a contact tracing framework that logs smart space users' co-existence using IoT devices as reference anchors. The design is non-intrusive as it relies on passive wireless interactions between each user's carried equipment (e.g., smartphone, wearable, proximity card) with an IoT device by utilizing received signal strength indicators (RSSI). The proposed framework can log the identities for the interacting pair, their estimated distance, and the overlapping time duration. Also, we propose a machine learning-based infection risk classification method to characterize each interaction that relies on RSSI-based attributes and contact details. Finally, the proposed contact tracing framework's performance is evaluated through a real-world case study of actual wireless interactions between users and IoT devices through Bluetooth Low Energy advertising. The results demonstrate the system's capability to accurately capture contact between mobile users and assess their infection risk provided adequate model training over time.
Robust Global Motion Estimation for Video Stabilization Based on Improved K-Means Clustering and Superpixel

Obtaining accurate global motion is a crucial step for video stabilization. This paper proposes a robust and simple method to implement global motion estimation. We don’t extend the framework of 2D video stabilization but add a “plug and play” module to motion estimation based on feature points. Firstly, simple linear iterative clustering (SLIC) pre-segmentation is used to obtain superpixels of the video frame, clustering is performed according to the superpixel centroid motion vector and cluster center with large value is eliminated. Secondly, in order to obtain accurate global motion estimation, an improved K-means clustering is proposed. We match the feature points of the remaining superpixels between two adjacent frames, establish a feature points’ motion vector space, and use improved K-means clustering for clustering. Finally, the richest cluster is being retained, and the global motion is obtained by homography transformation. Our proposed method has been verified on different types of videos and has efficient performance than traditional approaches. The stabilization video has an average improvement of 0.24 in the structural similarity index than the original video and 0.1 higher than the traditional method.
Federated Learning Application on Depression Treatment Robots(DTbot)

Depression is one of the most prevalent psychiatric disorders and an important public health problem. Its etiology is multifaceted, and the specific pathophysiological mechanisms are still unclear. At present, the main treatment methods for depression are medication, psychotherapy and physical therapy, and clinical applications usually combine two or three methods. Psychotherapy is currently mainly oriented towards the traditional face-to-face communication with psychologists, and is rarely combined with the current rapid development of technology. In this paper, we aim to design an intelligent robot that incorporates deep learning methods to help doctors treat patients more efficiently. The problem is that the current models of robots are trained by uploading data to a server, and then having the server train the robot. There are disadvantages of this approach. First, patient videos and conversations are private information. So uploading those private information to the server can lead to patient information leakage, which is bad. Second, the data recorded in daily life, including audio and video, are very large files that are slow to transfer and tend to cause package loss and other problems in the process. Training a multi-robot model in combination with federal learning would be a good solution to these two problems. The article combines federal learning with basic deep learning methods to design a depression treatment robot(DTbot) that can treat patients with more privacy and efficiency while handling their personal information.
DeepVista: 16K Panoramic Cinema on Your Mobile Device

In this paper, we design, implement, and evaluate , which is to our knowledge the first consumer-class system that streams panoramic videos far beyond the ultra high-definition resolution (up to 16K) to mobile devices, offering truly immersive experiences. Such an immense resolution makes streaming video-on-demand (VoD) content extremely resource-demanding. To tackle this challenge, introduces a novel framework that leverages an edge server to perform efficient, intelligent, and quality-guaranteed content transcoding, by extracting from panoramic frames the viewport stream that will be delivered to the client. To support real-time transcoding of 16K content, employs several key mechanisms such as dual-GPU acceleration, lossless viewport extraction, deep viewport prediction, and a two-layer streaming design. Our extensive evaluations using real users' viewport movement data indicate that outperforms existing solutions, and can smoothly stream 16K panoramic videos to mobile devices over diverse wireless networks including WiFi, LTE, and mmWave 5G.
Privacy-preserving Dynamic Symmetric Searchable Encryption with Controllable Leakage

Searchable Encryption (SE) is a technique that allows Cloud Service Providers to search over encrypted datasets without learning the content of queries and records. In recent years, many SE schemes have been proposed to protect outsourced data. However, most of them leak sensitive information, from which attackers could still infer the content of queries and records by mounting leakage-based inference attacks, such as the count attack and file-injection attack . In this work, first we define the leakage in searchable encrypted databases and analyse how the leakage is leveraged in existing leakage-based attacks. Second, we propose a &lt;underline&gt;P&lt;/underline&gt;rivacy-preserving &lt;underline&gt;M&lt;/underline&gt;ulti-&lt;underline&gt;c&lt;/underline&gt;loud based dynamic symmetric SE scheme for relational &lt;underline&gt;D&lt;/underline&gt;ata&lt;underline&gt;b&lt;/underline&gt;ase ( P-McDb ). P-McDb has minimal leakage, which not only ensures confidentiality of queries and records but also protects the search, intersection, and size patterns. Moreover, P-McDb ensures both forward and backward privacy of the database. Thus, P-McDb could resist existing leakage-based attacks, e.g., active file/record-injection attacks. We give security definition and analysis to show how P-McDb hides the aforementioned patterns. Finally, we implemented a prototype of P-McDb and tested it using the TPC-H benchmark dataset. Our evaluation results show that users can get the required records in 2.16 s when searching over 4.1 million records.
Code Smell Detection Using Whale Optimization Algorithm

Software systems have been employed in many fields as a means to reduce human efforts; consequently, stakeholders are interested in more updates of their capabilities. Code smells arise as one of the obstacles in the software industry. They are characteristics of software source code that indicate a deeper problem in design. These smells appear not only in the design but also in software implementation. Code smells introduce bugs, affect software maintainability, and lead to higher maintenance costs. Uncovering code smells can be formulated as an optimization problem of finding the best detection rules. Although researchers have recommended different techniques to improve the accuracy of code smell detection, these methods are still unstable and need to be improved. Previous research has sought only to discover a few at a time (three or five types) and did not set rules for detecting their types. Our research improves code smell detection by applying a search-based technique; we use the Whale Optimization Algorithm as a classifier to find ideal detection rules. Applying this algorithm, the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the within-class variance. The proposed framework adopts if-then detection rules during the software development life cycle. Those rules identify the types for both medium and large projects. Experiments are conducted on five open-source software projects to discover nine smell types that mostly appear in codes. The proposed detection framework has an average of 94.24% precision and 93.4% recall. These accurate values are better than other search-based algorithms of the same field. The proposed framework improves code smell detection, which increases software quality while minimizing maintenance effort, time, and cost. Additionally, the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.
Boosting Magnetic Resonance Image Denoising With Generative Adversarial Networks

Denoising plays an important role in the Magnetic Resonance Imaging (MRI) applications for medical diagnosis.MRI images usually contain undesired noises which would negatively affect the exactitude of pathological diagnosis.Recently, many models for MRI denoising have been developed from deep learning networks.In this paper, we propose a novel MRI image denoising method using the conditional Generative Adversarial Networks (GANs).Specifically, a Convolutional Neural Network (CNN) is utilized as the discriminator in the process to distinguish whether the image pair obtained from the conditional GAN is a real pair which consists of a noisy image and a noise-free image or a fake pair which, on the other hand, contains a noisy image and a denoised image.In our design, the convolutional encoder-decoder networks-based generator is used to remove the noise in the noisy MRI images as much as possible.The whole architecture is trained by adversarial learning.Experiments using both synthetic and real clinical MRI datasets are conducted.When tested on the synthetic T1w images with 10% noise level, our method performed better in terms of reaching a high structural similarity index (SSIM) at 0.9489 while that of the next best method was only 0.7485.Moreover, when the image noise level was increased from 1% to 10%, our method was more stable that the SSIM only dropped about 3.2% while that of the next best method dropped about 23.7%.Simulation results demonstrate that the proposed method is more robust and outperforms the conventional methods in both the denoising level and preservation of the anatomical structures and defined contrast.
Blockchain Transforming Cyber-Attacks: Healthcare Industry

Cyber-attacks mean those attacks launched on online users who are not security conscious either by using a computer network (spamming, phishing, etc.) Or as a tool to engage in criminal activities (cyberstalking or inside man, etc.). Cyber-attacks are on the increase thereby making cybersecurity a challenging task in this digital age. The Healthcare industry, been one of the largest industry has been facing a lot of cyber-attacks challenges ranging from malware attacks, Distributed Denial-of-Service (DDoS) attacks, and so on thereby leading to identity theft, data manipulation from an unauthorized entity. Electronic healthcare records are sensitive information that contains patient's personal information like name medication histories, lab reports, payment details, contact and home address which have to be shared among healthcare providers need to be efficiently secured from attacks. But the standard cybersecurity platform which is currently used by the healthcare industry to preserve information has a lot of flaws thereby rendering medical records venerable to cyber-attack. To address this problem there is a need to change the standard cybersecurity platform on the healthcare industry to blockchain-based security, which is one of the best solutions to cyber-attacks. We have done an in-depth analysis of cyber-attacks in the context of four stages and types of cyber-attacks threats. Blockchain technology can transform the healthcare industry by engaging with its Hyperledger fabric technology which will guarantee optimal security against all sorts of cyber-attacks. This paper will highlight how blockchain will transform cyber-attacks on the healthcare industry and with the recommendation of blockchain technology as a solution to cyber-attacks
DEKR

The huge number of machine learning (ML) methods has resulted in significant information overload. Faced with an overwhelming number of ML methods, it is challenging to select appropriate ones for the given dataset and task. In general, the names of ML methods or datasets are rather condensed, thus lacking specific explanations, while the rich latent relationships between ML entities are not fully explored. In this paper, we propose a description-enhanced machine learning knowledge graph-based approach - DEKR - to help recommend appropriate ML methods for given ML datasets. The proposed knowledge graph (KG) not only includes the connections between entities but also contains the descriptions of the dataset and method entities. DEKR fuses the structural information with the description information of entities in the knowledge graph. It is a deep hybrid recommendation framework, which incorporates the knowledge graph-based and text-based methods, overcoming the limitations of previous knowledge graph-based recommendation systems that ignore the description information. There are two key components of DEKR: 1) a graph neural network aggregating information from multi-order neighbors with attention to enrich the seed (i.e. dataset or method) node's own representation, and 2) a deep collaborative filtering network based on the description text to obtain the linear and nonlinear interactions of description features. Through extensive experiments, we demonstrated the efficiency of DEKR, which outperforms the current state-of-the-art baselines by a large margin.
U-RSNet: An unsupervised probabilistic model for joint registration and segmentation

Medical image segmentation and registration have vital roles in computer-assisted diagnosis procedures, challenging tasks suffering from various limitations and artifacts inside images. Recently, deep learning techniques accomplish these two tasks and achieve outstanding performances. However, most deep learning-based methods overlook the potential correlation between each other. In this paper, an unsupervised probabilistic model named U-RSNet is proposed to realize concurrent medical image registration and segmentation in one framework. Specifically, the unsupervised segmentation branch is derived from Bayesian inference. The prior warped atlas for segmentation can be obtained by deforming a known probabilistic atlas by the corresponding invertible deformation field with a well-behaved diffeomorphic guarantee, which can perfectly integrate these two tasks to form a complete intelligent prediction system. In this case, the segmentation performance could be largely improved based on the warped probabilistic atlas obtained from the registration branch. Experiments on human brain 3D magnetic resonance images have demonstrated the effectiveness of our approach. We trained and validated U-RSNet with 1000 images and tested its performances on four public datasets. We showed our method successfully realized concurrent segmentation and registration and yielded better segmentation results than a separately trained network.