Reviewerly/exHarmony huggingface dataset:
Special issue on Explainable Artificial Intelligence (XAI)

The special issue on Explainable Artificial Intelligence (XAI) provides a representative snapshot of the state of the art in the 2020-2021 time-frame and highlights future research directions. The scope of the special issue is intentionally broad, ranging from technical contributions to human-centered studies, from surveys to philosophical perspectives. A total of 97 papers were submitted for the special issue, of which 27 were finally accepted for publication after thorough peer review.
A treatment recommender clinical decision support system for personalized medicine: method development and proof-of-concept for drug resistant tuberculosis

Personalized medicine tailors care based on the patient's or pathogen's genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians.We developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting.We applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%.Our novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.
Controllable Medical Image Generation via GAN

Medical image data is critically important for a range of disciplines, including medical image perception research, clinician training programs, and computer vision algorithms, among many other applications. Authentic medical image data, unfortunately, is relatively scarce for many of these uses. Because of this, researchers often collect their own data in nearby hospitals, which limits the generalizabilty of the data and findings. Moreover, even when larger datasets become available, they are of limited use because of the necessary data processing procedures such as de-identification, labeling, and categorizing, which requires significant time and effort. Thus, in some applications, including behavioral experiments on medical image perception, researchers have used naive artificial medical images (e.g., shapes or textures that are not realistic). These artificial medical images are easy to generate and manipulate, but the lack of authenticity inevitably raises questions about the applicability of the research to clinical practice. Recently, with the great progress in Generative Adversarial Networks (GAN), authentic images can be generated with high quality. In this paper, we propose to use GAN to generate authentic medical images for medical imaging studies. We also adopt a controllable method to manipulate the generated image attributes such that these images can satisfy any arbitrary experimenter goals, tasks, or stimulus settings. We have tested the proposed method on various medical image modalities, including mammogram, MRI, CT, and skin cancer images. The generated authentic medical images verify the success of the proposed method. The model and generated images could be employed in any medical image perception research.
Parallel Genetic Algorithms’ Implementation Using a Scalable Concurrent Operation in Python

This paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master-Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are compared with the basic serial genetic algorithm model. Four modules, Multiprocessing, Celery, PyCSP, and Scalable Concurrent Operation in Python, were investigated among the many parallelization options in Python. The Scalable Concurrent Operation in Python was selected as the most favorable option, so the models were implemented using the Python programming language, RabbitMQ, and SCOOP. Based on the implementation results and testing performed, a comparison of the hardware utilization of each deployed model is provided. The results' implementation using SCOOP was investigated from three aspects. The first aspect was the parallelization and integration of the SCOOP module into the resulting Python module. The second was the communication within the genetic algorithm topology. The third aspect was the performance of the parallel genetic algorithm model depending on the hardware.
4-bit Conformer with Native Quantization Aware Training for Speech Recognition

Reducing the latency and model size has always been a significant research problem for live Automatic Speech Recognition (ASR) application scenarios. Along this direction, model quantization has become an increasingly popular approach to compress neural networks and reduce computation cost. Most of the existing practical ASR systems apply post-training 8-bit quantization. To achieve a higher compression rate without introducing additional performance regression, in this study, we propose to develop 4-bit ASR models with native quantization aware training, which leverages native integer operations to effectively optimize both training and inference. We conducted two experiments on state-of-the-art Conformer-based ASR models to evaluate our proposed quantization technique. First, we explored the impact of different precisions for both weight and activation quantization on the LibriSpeech dataset, and obtained a lossless 4-bit Conformer model with 7.7x size reduction compared to the float32 model. Following this, we for the first time investigated and revealed the viability of 4-bit quantization on a practical ASR system that is trained with large-scale datasets, and produced a lossless Conformer ASR model with mixed 4-bit and 8-bit weights that has 5x size reduction compared to the float32 model.
TAHDNet: Time-aware hierarchical dependency network for medication recommendation

Medication recommendation is a hot topic in the research of applying neural networks to the healthcare area. Although extensive progressions have been made, current researches still face the following challenges: (i). Existing methods are poor at efficiently capturing and leveraging local and global dependency information from patient visit records. (ii). Current time-aware models based on irregularly interval medical records tend to ignore periodic variability in patient conditions, which limits the representational learning capability of these models. Therefore, we propose a Dynamic Time-aware Hierarchical Dependency Network (TAHDNet) for the medication recommendation task to address these challenges. Firstly, we use a Transformer-based model to learn the global information of the whole patient record through a self-supervised pre-training process. Secondly, a 1D-CNN model is used to learn the local dependencies on visitation level. Thirdly, we propose a dynamic time-aware module with a fused temporal decay function to assign different weights among different time intervals dynamically through a key-value attention mechanism. Experimental results on real-world datasets demonstrate the effectiveness of the model proposed in this paper.
A simple neural vector space model for medical concept normalization using concept embeddings

Medical concept normalization (MCN), the task of linking textual mentions to concepts in an ontology, provides a solution to unify different ways of referring to the same concept. In this paper, we present a simple neural MCN model that takes mentions as input and directly predicts concepts. We evaluate our proposed model on clinical datasets from ShARe/CLEF eHealth 2013 shared task and 2019 n2c2/OHNLP shared task track 3. Our neural MCN model consists of an encoder, and a normalized temperature-scaled softmax (NT-softmax) layer that maximizes the cosine similarity score of matching the mention to the correct concept. We adopt SAPBERT as the encoder and initialize the weights in the NT-softmax layer with pre-computed concept embeddings from SAPBERT. Our proposed neural model achieves competitive performance on ShARe/CLEF 2013 and establishes a new state-of-the-art on 2019-n2c2-MCN. Yet this model is simpler than most prior work: it requires no complex pipelines, no hand-crafted rules, and no preprocessing, making it simpler to apply in new settings. Analyses of our proposed model show that the NT-softmax is better than the conventional softmax on the MCN task, and both the CUI-less threshold parameter and the initialization of the weight vectors in the NT-softmax layer contribute to the improvements. We propose a simple neural model for clinical MCN, an one-step approach with simpler inference and more effective performance than prior work. Our analyses demonstrate future work on MCN may require more effort on unseen concepts.
Blockchain-based continuous data integrity checking protocol with zero-knowledge privacy protection

The cloud computing technology has emerged, developed, and matured in recent years, consequently commercializing remote outsourcing storage services. An increasing number of companies and individuals have chosen the cloud to store their data. However, accidents, such as cloud server downtime, cloud data loss, and accidental deletion, are serious issues for some applications that need to run around the clock. For some mission and business-critical applications, the continuous availability of outsourcing storage services is also necessary to protect users' outsourced data during downtime. Nevertheless, ensuring the continuous availability of data in public cloud data integrity auditing protocols leads to data privacy issues because auditors can obtain the data content of users by a sufficient number of storage proofs. Therefore, protecting data privacy is a burning issue. In addition, existing data integrity auditing schemes that rely on semi-trusted third-party auditors have several security problems, including single points of failure and performance bottlenecks. To deal with these issues, we propose herein a blockchain-based continuous data integrity checking protocol with zero-knowledge privacy protection. We realize a concrete construction by using a verifiable delay function with high efficiency and proof of retrievability, and prove the security of the proposal in a random oracle model. The proposed construction supports dynamic updates for the outsourced data. We also design smart contracts to ensure fairness among the parties involved. Finally, we implement the protocols, and the experimental results demonstrate the efficiency of the proposed protocol.
EmpHi: Generating Empathetic Responses with Human-like Intents

In empathetic conversations, humans express their empathy to others with empathetic intents.However, most existing empathetic conversational methods suffer from a lack of empathetic intents, which leads to monotonous empathy.To address the bias of the empathetic intents distribution between empathetic dialogue models and humans, we propose a novel model to generate empathetic responses with humanconsistent empathetic intents, EmpHi for short.Precisely, EmpHi learns the distribution of potential empathetic intents with a discrete latent variable, then combines both implicit and explicit intent representation to generate responses with various empathetic intents.Experiments show that EmpHi outperforms state-ofthe-art models in terms of empathy, relevance, and diversity on both automatic and human evaluation.Moreover, the case studies demonstrate the high interpretability and outstanding performance of our model.Our code are avaliable at https://github.com/mattc95/EmpHi.
A Weighted Heterogeneous Graph-Based Dialog System

Knowledge-based dialog systems have attracted increasing research interest in diverse applications. However, for disease diagnosis, the widely used knowledge graph (KG) is hard to represent the symptom–symptom and symptom–disease relations since the edges of traditional KG are unweighted. Most research on disease diagnosis dialog systems highly relies on data-driven methods and statistical features, lacking profound comprehension of symptom–symptom and symptom–disease relations. To tackle this issue, this work presents a weighted heterogeneous graph-based dialog system for disease diagnosis. Specifically, we build a weighted heterogeneous graph based on symptom co-occurrence and the proposed symptom frequency-inverse disease frequency. Then, this work proposes a graph-based deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (graph-DQN) for dialog management. By combining graph convolutional network (GCN) with DQN to learn the embeddings of diseases and symptoms from both the structural and attribute information in the weighted heterogeneous graph, graph-DQN could capture the symptom–disease relations and symptom–symptom relations better. Experimental results show that the proposed dialog system rivals the state-of-the-art models. More importantly, the proposed dialog system can complete the task with fewer dialog turns and possess a better distinguishing capability on diseases with similar symptoms.
A new multi-objective artificial bee colony algorithm based on reference point and opposition

A new multi-objective artificial bee colony (ABC) algorithm based on reference point and opposition (called ROMOABC) is proposed in this paper. Firstly, the original framework of ABC is modified to improve the efficiency of population renewal and accelerate the convergence rate. On the basis of this framework, two new strategies are proposed. In the scout bee search, opposition-based learning and elite solutions are used to reduce the waste of computing resources. Distribution of solutions is improved by using reference points' associated external archive. Experiments are conducted on 16 multi-objective benchmark functions including ZDT, DTLZ and WFG multi-objective benchmark functions. The comparison of ROMOABC with five other multi-objective algorithms shows that it has competitive convergence and diversity.
Learning Accurate, Speedy, Lightweight CNNs via Instance-Specific Multi-Teacher Knowledge Distillation for Distracted Driver Posture Identification

For deployment on an embedded processor for distracted driver classification, the model should satisfy the demand for both high accuracy, real-time inference, and limited storage resources. Conventional deep CNN models such as VGG, ResNet, DenseNet, often aim for high accuracy, making their model heavy for an embedded system with limited memory space and computing resources. In contrast, lightweight models are greatly compressed but at a significant sacrifice of accuracy. To bridge this gap, we propose an instance-specific multi-teacher knowledge distillation model (IsMt-KD) to learn more accurate, speedy, and lightweight CNNs for distracted driver posture classification. Specifically, in multi-teacher knowledge distillation, most of the current approaches either randomly select a teacher model and apply the prediction of such teacher model as the soft-label or allocate an equal weight to every teacher model and average all the predictions of the teachers as the soft label. In this paper, we observe that, when facing the same instance, the outputs of different teachers vary greatly, in which some teachers can predict it right whereas the others may give pretty high probabilities to the irrelevant classes. Thus, it is inappropriate to set fixed weights or the same weights for teachers. To this end, a simple yet effective instance-specific teacher grading module is designed to dynamically assign weights to teacher models based on individual instances. In this way, we can dynamically distill the knowledge from multiple teachers by considering both instance-specific high-level and instance-specific intermediate-level information. Our extensive experimental results on AUC and StateFarm datasets, and our implementation on edge hardware platforms including HUAWEI MediaPad c5 and Nvidia Jetson TX2, verify the effectiveness and feasibility of our approach.
DeepCyto: a hybrid framework for cervical cancer classification by using deep feature fusion of cytology images

Cervical cancer is the second most commonly seen cancer in women. It affects the cervix portion of the vagina. The most preferred diagnostic test required for screening cervical cancer is the pap smear test. Pap smear is a time-consuming test as it requires detailed analysis by expert cytologists. Cytologists can screen around 100 to 1000 slides depending upon the availability of advanced equipment. Due to this reason Artificial intelligence (AI) based computer-aided diagnosis system for the classification of pap smear images is needed. There are some AI-based solutions proposed in the literature, still an effective and accurate system is under research. In this paper, the deep learning-based hybrid methodology namely DeepCyto is proposed for the classification of pap smear cytology images. The DeepCyto extracts the feature fusion vectors from pre-trained models and passes these to two workflows. Workflow-1 applies principal component analysis and machine learning ensemble to classify the pap smear images. Workflow-2 takes feature fusion vectors as an input and applies an artificial neural network for classification. The experiments are performed on three benchmark datasets namely Herlev, SipakMed, and LBCs. The performance measures of accuracy, precision, recall and F1-score are used to evaluate the effectiveness of the DeepCyto. The experimental results depict that Workflow-2 has given the best performance on all three datasets even with a smaller number of epochs. Also, the performance of the DeepCyto Workflow 2 on multi-cell images of LBCs is better compared to single cell images of other datasets. Thus, DeepCyto is an efficient method for accurate feature extraction as well as pap smear image classification.
Shaping the Future of the Application of Quantum Computing in Intelligent Transportation System

The intelligent transportation system (ITS) integrates a variety of advanced science and technology to support and monitor road traffic systems and accelerate the urbanization process of various countries. This paper analyzes the shortcomings of ITS, introduces the principle of quantum computing and the performance of universal quantum computer and special-purpose quantum computer, and shows how to use quantum advantages to improve the existing ITS. The application of quantum computer in transportation field is reviewed from three application directions: path planning, transportation operation management, and transportation facility layout. Due to the slow development of the current universal quantum computer, the D-Wave quantum machine is used as a breakthrough in the practical application. This paper makes it clear that quantum computing is a powerful tool to promote the development of ITS, emphasizes the importance and necessity of introducing quantum computing into intelligent transportation, and discusses the possible development direction in the future.
Public Auditing for Secure Cloud Storage using MD5 Algorithm

Cloud computing has become a significant technology trend, and plenty of consultants expect that cloud computing can reshape data technology (IT) and also the IT marketplace. In this paper, we are suggesting a safety mechanism that provides open analysis on shared knowledge within the cloud. During this mechanism, the individual of the underwriter on every sq. in shared statistics are going to be hold on in private from open verifies, who can proficiently verify the shared records without improving the entire report. This mechanism can play out numerous comparing undertakings at the identical time instead of checking them separately. This framework provides a security saving options of open inspecting part for shared facts at intervals the cloud. we have a tendency to area unit mistreatment ring marks to make homomorphy authentication, so thereto, AN open verify will offer an summary on shared data while not obtaining the entire facts, but it cannot be understanding that who is the underwriter on every piece. To get the effectiveness of confirming numerous examining undertakings, we facilitate to increase our tool for better examining. Certainly, the tractability implies the capacity to gather administrator to find the person of the underwriter in the metadata of few great instances.
Blockchain-based Secure Big Data Storage on Cloud

In the cryptocurrency era, Blockchain is one of the expeditiously growing information technologies that help in providing security to the data. Data tampering and authentication problems generally occur in centralized servers while sharing and storing the data. Blockchain provides the platform for big data and cloud storage in enhancing the security by evading from pernicious users. In this paper, we have discussed the exhaustive description of blockchain and its need, features and applications. Analysis of blockchain is done for different domains such as big data, cloud, internet of things and mobile cloud where the differences V’s are compared with big data and blockchain. SWOT (Strength Weakness Opportunities Threats) analysis is performed to address the merits and limitations in blockchain technology. The survey in aspects of data security, data storage, data sharing and data authentication through blockchain technology is done and the challenges are discussed to overcome the problem that leads in big data and cloud storage. The detailed comparative analysis proves that the blockchain technology overcomes the problems in big data storage and data security in cloud.