Reviewerly/exHarmony huggingface dataset:
A dataset of simulated patient-physician medical interviews with a focus on respiratory cases

Artificial Intelligence (AI) is playing a major role in medical education, diagnosis, and outbreak detection through Natural Language Processing (NLP), machine learning models and deep learning tools. However, in order to train AI to facilitate these medical fields, well-documented and accurate medical conversations are needed. The dataset presented covers a series of medical conversations in the format of Objective Structured Clinical Examinations (OSCE), with a focus on respiratory cases in audio format and corresponding text documents. These cases were simulated, recorded, transcribed, and manually corrected with the underlying aim of providing a comprehensive set of medical conversation data to the academic and industry community. Potential applications include speech recognition detection for speech-to-text errors, training NLP models to extract symptoms, detecting diseases, or for educational purposes, including training an avatar to converse with healthcare professional students as a standardized patient during clinical examinations. The application opportunities for the presented dataset are vast, given that this calibre of data is difficult to access and costly to develop.
Facial landmark disentangled network with variational autoencoder

Abstract Learning disentangled representation of data is a key problem in deep learning. Specifically, disentangling 2D facial landmarks into different factors ( e.g. , identity and expression) is widely used in the applications of face reconstruction, face reenactment and talking head et al. . However, due to the sparsity of landmarks and the lack of accurate labels for the factors, it is hard to learn the disentangled representation of landmarks. To address these problem, we propose a simple and effective model named FLD-VAE to disentangle arbitrary facial landmarks into identity and expression latent representations, which is based on a Variational Autoencoder framework. Besides, we propose three invariant loss functions in both latent and data levels to constrain the invariance of representations during training stage. Moreover, we implement an identity preservation loss to further enhance the representation ability of identity factor. To the best of our knowledge, this is the first work to end-to-end disentangle identity and expression factors simultaneously from one single facial landmark.
PPDMIT: a lightweight architecture for privacy-preserving data aggregation in the Internet of Things

Data is generated over time by each device in the Internet of Things (IoT) ecosphere. Recent years have seen a resurgence in interest in the IoT due to its positive impact on society. However, due to the automatic management of IoT devices, the possibility of disclosing sensitive information without user consent is high. A situation in which information should not be unintentionally disclosed to outside parties we do not trust, i.e., privacy-preservation. Additionally, IoT devices should share their data with others to perform data aggregation and provide high-level services. There is a trade-off between the amount of data utility and the amount of disclosure of data. This trade-off has been causing a big challenge in this field. To improve the efficiency of this trade-off rather than current studies, in this study, we propose a Privacy-Preserving Data Aggregation architecture, PPDMIT, that leverages Homomorphic Paillier Encryption (HPE), K-means, a One-way hash chain, and the Chinese Remainder Theorem (CRT). We have found that the proposed privacy-preserving architecture achieves more efficient data aggregation than current studies and improves privacy preservation by utilizing extensive simulations. Moreover, we found that our proposed architecture is highly applicable to IoT environments while preventing unauthorized data disclosure. Specifically, our solution depicted an 8.096% improvement over LPDA and 6.508% over PPIOT.
Lightweight single image deraining algorithm incorporating visual saliency

There are still some challenges in the task of single image rain removal, such as artefact remnant, background over-smooth, and increasingly complex and heavy-weight network architecture. Especially too heavy-weight network to fit outdoor detection devices or mobile devices. To address the above challenges, we propose a lightweight single image Deraining algorithm incorporating visual attention saliency mechanisms (LDVS). The proposed network consists of five blocks and two convolution operations, where each block consists of a dilation convolution module and a convolutional block attention module (CBAM). Specifically, visual saliency module CBAM is used for accurate localization of rain streak, and further the combinations of dilated convolution with CBAM is used to extract feature maps of rain streaks faithfully, which is able to remove artefact remnant while maintaining background details. A good tradeoff is presented between the network's weight size and effect of rain removal. Specifically, with only 48,268 parameters, the proposed model can achieve a guaranteed performance. Extensive experiments on a few typical rainy scenarios on synthetic and real-world datasets have demonstrated that to achieve the same level of performance, the proposed method has far smaller size than most of the baselines under both qualitative and quantitative analyses.
Muscle‐driven virtual human motion generation approach based on deep reinforcement learning

Abstract We propose a muscle‐driven motion generation approach to realize virtual human motion with user interaction and higher fidelity, which can address the problem that the joint‐driven fails to reflect the motion process of the human body. First, a simplified virtual human musculoskeletal model is built based on human biomechanics. Then, a hierarchical policy learning framework is constructed including motion tracking layer, SPD controller and muscle control layer. The motion tracking layer is responsible for mimicking reference motion and completing control command, using proximal policy optimization to train the policy; the muscle control layer is aimed to minimize muscle energy consumption and train the policy based on supervised learning; the SPD controller acts as a link between the two layers. At the same time, we integrate the curriculum learning to improve the efficiency and success rate of policy training. Simulation experiments show that the proposed approach can use motion capture data and pose estimation data as reference motions to generate better and more adaptable motions. Furthermore, the virtual human has the ability to respond to the user control command during the motion, and can complete the target task successfully.
Stance Detection with a Multi-Target Adversarial Attention Network

Stance detection aims to assign a stance label (in favor or against) to a post towards a specific target. In the literature, there are many studies focusing on this topic, and most of them treat stance detection as a supervised learning task. Therefore, a new classifier needs to be built from scratch on a well-prepared set of ground-truth data whenever predictions are needed for an unseen target. However, it is difficult to annotate the stance of a post, since a stance is a subjective attitude towards a target. Hence, it is necessary to learn the information from unlabeled data or other target data to help stance detection with a certain target. In this study, we propose a multi-target stance detection framework to integrate multi-target data together for stance detection. Since topic and sentiment are two important factors to identify the stance of a post in multi-target data, we propose an adversarial attention network to integrate multi-target data by detecting and connecting topic and sentiment information. In particular, the adversarial network is utilized to determine the topic and the sentiment of each post to collect some target-invariant information for stance detection. In addition, the attention mechanism is utilized to connect posts with a similar topic or sentiment to acquire some key information for stance detection. The experimental results not only demonstrate the effectiveness of the proposed model, but also indicate the importance of the topic and the sentiment information for stance detection using multi-target data.
The Embedding of Sports Social Organizations in Rural Governance Based on the Collaborative Governance Model of Multiple Subjects

Rural governance is an important part of national governance, and the participation of social organizations is also very important. This research explores the multiple relationships between rural governance subjects and governance methods through the basic requirements of “co-construction and sharing” of multi-governance and the status quo of creation activities, integrates an innovative rural community governance evaluation and supervision system, and builds rural community governance evaluation and supervision system mechanism. Mutually exclusive constraints on the diversity of governance subjects will provide strategic guidance for sports social organizations to participate in diversified management.
Application tool for assisting diagnosis and forecast progression of abnormality and medical condition of patients

The precocious knowledge on reckoning schemes scientifically and technically was replaced with the rapid amelioration in medicinal contrivance systems and treatment information registers. On average the management accomplishes hospital information system (HIS) and picture archiving and communication systems (PACS) using digital entities to maintain medical examinations, images and statements. Such systems even though are much helpful for the hospital staff, doctor away from the hospital have lot of difficulty to access the patient data during risk facing situations. To iron out the above-mentioned complications, a new idea of the proposed framework has been disseminated for medical descriptions to the doctors at inaccessible areas. This organization requires a hand-hand PDA’s that adopts CDMA techniques with mobile networks. This arrangement requires a server and PDA for its implementation. This model supports the entire reports of the patient to the doctors located even at remote location with accessibility and doctor information reliable for patients. PDA created will disclose the inmate’s medicinal reports and scans over an outlying network. To corroborate or validate user data, remote data approach is applied by PDA that connects the server directory and for relegation of information from the server, uses file transfer protocol. From testing, observations show ninety seconds to convey thirty images with a fineness of 832 x 488, intensity of 24 bits and magnitude of 0.37 Mb. This idea proposed using bitmap image acquiring technique demonstrates no difficulty for distant doctors to collect and examine the sufferer images right away at the time of crisis or urgencies, which is completely subjective analysis based on doctor’s point of view.
A novelty detection approach to effectively predict conversion from mild cognitive impairment to Alzheimer’s disease

Abstract Accurately recognising patients with progressive mild cognitive impairment (pMCI) who will develop Alzheimer’s disease (AD) in subsequent years is very important, as early identification of those patients will enable interventions to potentially reduce the number of those transitioning from MCI to AD. Most studies in this area have concentrated on high-dimensional neuroimaging data with supervised binary/multi-class classification algorithms. However, neuroimaging data is more costly to obtain than non-imaging, and healthcare datasets are normally imbalanced which may reduce classification performance and reliability. To address these challenges, we proposed a new strategy that employs unsupervised novelty detection (ND) techniques to predict pMCI from the AD neuroimaging initiative non-imaging data. ND algorithms, including the k-nearest neighbours (kNN), k-means, Gaussian mixture model (GMM), isolation forest (IF) and extreme learning machine (ELM), were employed and compared with supervised binary support vector machine (SVM) and random forest (RF). We introduced optimisation with nested cross-validation and focused on maximising the adjusted F measure to ensure maximum generalisation of the proposed system by minimising false negative rates. Our extensive experimental results show that ND algorithms (0.727 ± 0.029 kNN, 0.7179 ± 0.0523 GMM, 0.7276 ± 0.0281 ELM) obtained comparable performance to supervised binary SVM (0.7359 ± 0.0451) with 20% stable MCI misclassification tolerance and were significantly better than RF (0.4771 ± 0.0167). Moreover, we found that the non-invasive, readily obtainable, and cost-effective cognitive and functional assessment was the most efficient predictor for predicting the pMCI within 2 years with ND techniques. Importantly, we presented an accessible and cost-effective approach to pMCI prediction, which does not require labelled data.
Survey on Recommender Systems Incorporating Trust

Large amount of information generated on the web is useful for extracting useful patterns about customers and their purchases. Recommender system provides framework to utilize this information to make suggestions to user according to their previous preferences. They are intelligent systems having decision making capabilities. This in turn enhances business profit. Recommender system endure from problems like cold start, fake profile generation and data sparsity. Inclusion of trust in recommender system helps to alleviate these problems to a great extent. The phenomenon of trust is derived from daily life exp eriences like believing the views/reviews suggested by friends and relatives for buying new things. The desideratum of this research pap er is to procure a survey on how trust can be incorporated in recommender systems and the advantages trust aware recommender sy stems have over traditional recommender systems. It highlights the techniques that have been used to develop trust aware recommenders and pros and cones of these techniques.
Multi-user image retrieval with suppression of search pattern leakage

In image retrieval applications for multi-users, users can retrieve similar images in a database. The server can collude with the user and exploit the search patterns to infer the information of other users, which may cause search pattern leakage. However, current forward privacy solutions can only partially solve the problem. In this paper, a multi-user image retrieval scheme, called multi-user image retrieval with suppression of search pattern (MIRSP), is presented to suppress the search pattern leakage. This scheme uses a blockchain structure to establish an optimized tree index. Meanwhile, compressive sensing is used to convert high-dimensional feature vectors into similarity-preserving binary codes, which reduces the similarity calculation cost and guarantees the security of nodes. Then, a path shuffling algorithm is proposed to hide the identifier and position of the search nodes. Finally, a fully trusted certificate authority center is added to ensure the security management and distribution of keys and avoid unnecessary communication overhead. Our experimental evaluation shows that the proposed MIRSP is 3 × faster than the state-of-the-art schemes in search computation times, which demonstrates that MIRSP contributes significantly in providing security and privacy protection in multi-user scenarios.
Decentralized Online Voting System using Blockchain

Casting a vote is not only the right of a citizen but also a responsibility of the citizen. The citizens of a country get an opportunity to vote for their representatives who will represent the needs and suggestions of citizens. These representatives are elected through the process called elections. The traditional electoral system necessitates the actual presence of the voter which causes discomfort to the physically challenged people. Also, there are chances of vote tampering. This paper discusses the proposed solution that will solve the above problems. Our proposed solution is to use an Online Voting System using Ethereum Blockchain. This web-based voting system helps the voters to vote from any location. The system validates the voters with the help of their Aadhar cards linked with their voter id. After which the system requires the voters to scan their face and fingerprint which will be verified with the data in the database. Blockchain technology encrypts the vote and thus it prevents every vote from tampering. It makes sure that a voter can vote only once for one candidate. The system fetches the election results quickly and thus reduces the labour cost and counting errors.
Image Resolution Enhancer using Deep Learning

Image Super-Resolution is a technique that is used to obtain high-resolution, realistic images from low-resolution input images. Deep learning algorithms such as SRCNN, ESRGAN, RDN, etc. have shown significant results in this field. But these algorithms at times vary in results. To solve this problem, this research study has proposed an image super-resolution by using Patch Extraction on Deep Learning Algorithm, in which the LR image is first divided into patches and then the algorithms like RDN and ESRGAN are applied. Comparing each patch from each algorithm based on PSNR values, the patch with the highest PSNR value will be selected. After picking up all the patches for that image, it will be reconstructed and hence the super-resolution image will be obtained as the output.
Fall Detection for The Elderly using YOLOv4 and LSTM

In elderly society, the falling of the elderly inside the house is the one of the causes of injury or death to the elderly. It negatively affects both the family and the elderly themselves. Therefore, taking care of the elderly at home is extremely important. In this paper, we used YOLOv4 technique to detect posture and LSTM to detect sequence of postures. YOLOv4 is the most efficient detection model compared to other detection models. LSTM is helpful in learning temporal features. Tests with 2 fall datasets showed an efficiency of posture detection by YOLOv4 at 87.88% and YOLOv4 and efficiency of fall detection by combination of YOLOv4 and LSTM at 100%.
Intelligent Fuzzy Network for Dynamic Sign Words Recognition from Spatial Features

Dynamic sign words recognition (DSR) from Spatial features using skeletal hand joints information play an important role in similarity problem of sign words. Unfortunately, recognizing dynamic sign words from spatial features are challenging as movement features have variety of complex appearances. Existing methods do not capture spatial interactions and dependencies among the estimates, which are extremely significant for DSR. To solve this problem, since spatial frames contain non-periodic and fast transient features, we decompose features into 3D wavelet transform (3D-WT) signals. 3D-WT maps each frame into other basis functions, to encode spatial relationship. 3D-WT features are fuzzified and utilized as input vector in Adaptive-neuro fuzzy inference system (ANFIS), which adaptively learn 3D spatial characteristic of hand joints. Then, ANFIS match and fuse features by iterative learning, which learns rich contextual information to recognize dynamic sign words. ANFIS achieved recognition accuracy of 94.08% and 93.16% for single and double-hand words. Extensive experiments by proposed approach on public data sets outperform some existing methods.
A Methodical Literature Survey on Block Chain-based Public Auditing in Cloud: Analysis on Performance and Door towards Future Scope

Cloud computing does an efficient program to offer and manage the resources owed to statistics and communication approaches for remote users. It exploits features of virtualization approaches over a number of shared resources like services, computing power, storage, etc. Huge attention towards cloud computing has drawn in both individual users and business organizations as it can effectively offer services in the way of public requirements including water and electricity. Cloud storage has the ability to effectively manage their remote data but, it faces the risk of being tampered with. Thus, this paper reviews several blockchain techniques to develop public auditing schemes for confirming data integrity in cloud storage. The major scope of this survey is to focus on existing auditing techniques and simulation tools in different contributions. Further, the performance analysis with different performance metrics from diverse contributions is pointed out. Thus, this review article presents a detailed overview of the various security properties that have been exploited in the cloud with the aim of enhancing the block chain-based public auditing schemes. Corresponding comparative results and computational and communication cost complexities for all these public auditing schemes are presented and discussed in this paper. Further, the open research directions for future investigation are also clearly described.
A Static Analysis Framework for Data Science Notebooks

Notebooks provide an interactive environment for programmers to develop code, analyse data and inject interleaved visualisations in a single environment. Despite their flexibility, a major pitfall that data scientists encounter is unexpected behaviour caused by the unique out-of-order execution model of notebooks. As a result, data scientists face various challenges ranging from notebook correctness, reproducibility and cleaning. In this paper, we propose a framework that performs static analysis on notebooks, incorporating their unique execution semantics. Our framework is general in the sense that it accommodates a wide range of analyses, useful for various notebook use cases. We have instantiated our framework on a diverse set of analyses and have evaluated them on 2211 real world notebooks. Our evaluation demonstrates that the vast majority (98.7%) of notebooks can be analysed in less than a second, well within the time frame required by interactive notebook clients.
Vehicle Detection for Unmanned Systems Based on Multimodal Feature Fusion

This paper proposes a 3D vehicle-detection algorithm based on multimodal feature fusion to address the problem of low vehicle-detection accuracy in unmanned system environment awareness. The algorithm matches the coordinate relationships between the two sensors and reduces sampling errors by combining the millimeter-wave radar and camera calibration. Statistical filtering is used to remove redundant points from the millimeter-wave radar data to reduce outlier interference; a multimodal feature fusion module is constructed to fuse the point cloud and image information using pixel-by-pixel averaging. Moreover, feature pyramids are added to extract fused high-level feature information, which is used to improve detection accuracy in complex road scenarios. A feature fusion region proposal structure was established to generate region proposals based on the high-level feature information. The vehicle detection results were obtained by matching the detection frames in their vertices after removal of the redundant detection frames using non-maximum suppression. Experimental results from the KITTI dataset show that the proposed method improved the efficiency and accuracy of vehicle detection with the corresponding average of 0.14 s and 84.71%.