Reviewerly/exHarmony huggingface dataset:
RELAXNet: Residual efficient learning and attention expected fusion network for real-time semantic segmentation

As a dense prediction problem, semantic segmentation consumes extensive memory and computational resources. However, the application of semantic segmentation requires the model to perform real-time analyses in portable devices, thus it is crucial to seek a trade-off between segmentation accuracy and inference speed. In this paper, we propose a lightweight semantic segmentation method based on attention mechanism to address this problem. First, we use novel Efficient Bottleneck Residual (EBR) Module and Efficient Asymmetric Bottleneck Residual (EABR) Module to extract both local and contextual information,which adopt a well-designed combination of depth-wise convolution, dilated convolution and factorized convolution, with channel shuffle to boost information interaction. Second, we introduce attention mechanism into skip connection between the encoder and decoder to promote reasonable fusion of high-level and low-level features, which furtherly enhance the accuracy. With only 1.9 M parameters, our model obtains 74.8% mIoU and 64 FPS running speed on Cityscapes dataset and 71.2% mIoU and 79 FPS running speed on Camvid dataset. Experiments demonstrate that our model achieves competitive results in terms of segmentation accuracy and running speed while controlling parameters.
Detecting Covid-19 chaos driven phishing/malicious URL attacks by a fuzzy logic and data mining based intelligence system

With confusion and uncertainty ruling the world, 2020 created near-perfect conditions for cybercriminals. As businesses virtually eliminated in-person experiences, the COVID-19 pandemic changed the way we live and caused a mass migration to digital platforms. However, this shift also made people more vulnerable to cyber-crime. Victims are being targeted by attackers for their credentials or financial rewards, or both. This is because the Internet itself is inherently difficult to secure, and the attackers can code in a way that exploits its flaws. Once the attackers gain root access to the devices, they have complete control and can do whatever they want. Consequently, taking advantage of highly unprecedented circumstances created by the Covid-19 event, cybercriminals launched massive phishing, malware, identity theft, and ransomware attacks. Therefore, if we wish to save people from these frauds in times when millions have already been tipped into poverty and the rest are trying hard to sustain, it is imperative to curb these attacks and attackers. This paper analyses the impact of Covid-19 on various cyber-security related aspects and sketches out the timeline of Covid-19 themed cyber-attacks launched globally to identify the modus operandi of the attackers and the impact of attacks. It also offers a thoroughly researched set of mitigation strategies which can be employed to prevent the attacks in the first place. Moreover, this manuscript proposes a fuzzy logic and data mining-based intelligence system for detecting Covid-19 themed malicious URL/phishing attacks. The performance of the system has been evaluated against various malicious/phishing URLs, and it was observed that the proposed system is a viable solution to this problem.
A hybrid E-learning recommendation integrating adaptive profiling and sentiment analysis

This research proposes a novel framework named Enhanced e-Learning Hybrid Recommender System (ELHRS) that provides an appropriate e-content with the highest predicted ratings corresponding to the learner’s particular needs. To accomplish this, a new model is developed to deduce the Semantic Learner Profile automatically. It adaptively associates the learning patterns and rules depending on the learner’s behavior and the semantic relations computed in the semantic matrix that mutually links e-learning materials and terms. Here, a semantic-based approach for term expansion is introduced using DBpedia and WordNet ontologies. Further, various sentiment analysis models are proposed and incorporated as a part of the recommender system to predict ratings of e-learning resources from posted text reviews utilizing fine-grained sentiment classification on five discrete classes. Qualitative Natural Language Processing (NLP) methods with tailored-made Convolutional Neural Network (CNN) are developed and evaluated on our customized dataset collected for a specific domain and a public dataset. Two improved language models are introduced depending on Skip-Gram (S-G) and Continuous Bag of Words (CBOW) techniques. In addition, a robust language model based on hybridization of these couple of methods is developed to derive better vocabulary representation, yielding better accuracy 89.1% for the CNN-Three-Channel-Concatenation model. The suggested recommendation methodology depends on the learner’s preferences, other similar learners’ experience and background, deriving their opinions from the reviews towards the best learning resources. This assists the learners in finding the desired e-content at the proper time.
Validity and Rigour in Soma Design-Sketching with the Soma

We report on the design processes of two ongoing soma design projects: the Pelvic Chair and the Breathing Wings. These projects take a first-person, soma design approach, grounded in a holistic perspective of the mind and body (the soma). We contribute a reflective account of our soma design processes that deepens the field’s understanding of how soma design is achieved through first-person approaches. We show how we use our somas, our first-person experiences, to stimulate a design process, to prototype through and to use as a way of critiquing emerging designs. Grounding our analysis in new materialism, we show how our designs are in essence, “performative intra-actions”. Using our own somas, our designs open up for experiences within certain constraints, allowing for a material-discursive agency of sorts. Many different somas may be intra-acted through our designs, even if it was our somas who started them.
Multi-UAV Coverage Path Planning Based on Hexagonal Grid Decomposition in Maritime Search and Rescue

In the event of a maritime accident, surveying the maximum area efficiently in the least amount of time is crucial for rescuing survivors. Increasingly, unmanned aerial vehicles (UAVs) are being used in search and rescue operations. This study proposes a method to generate a search path that covers all generated nodes in the shortest amount of time with multiple heterogeneous UAVs. The proposed model, which is a mixed-integer linear programming (MILP) model based on a hexagonal grid-based decomposition method, was verified through a simulation analysis based on the performance of an actual UAV. This study presents both the optimization technique’s calculation time as a function of the search area size and the various UAV routes derived as the search area grows. The results of this study can have wide-ranging applications for emergency search and rescue operations.
Blockchain Technology for Enhancing Traceability and Efficiency in Automobile Supply Chain—A Case Study

A robust traceability system would help organizations in inventory optimization reduce lead time and improve customer service and quality which further enables the organizations to be a leader in their industry sector. This research study analyzes the challenges faced by the automotive industry in its supply chain operations. Further, the traceability issues and waiting time at different nodes of the supply chain are considered to be priority issues that affect the overall supply chain efficiency in the automotive supply chain. After studying the existing blockchain architectures and their implementation methodology, this study proposes a new blockchain-based architecture to improve traceability and reduce waiting time for the automotive supply chain. A hyper ledger fabric-based blockchain architecture is developed to track the ownership transfers in inbound and outbound logistics. The simulation results of the proposed hyper ledger fabric-based blockchain architecture show that there is an improvement in the traceability of items at different nodes of the supply chain that enhances the Inventory Quality Ratio (IQR) and the mean waiting time is reduced at the factory, wholesaler, and retailer, which thereby improves the overall supply chain efficiency. The blockchain embedded supply chain is more capable to eliminate the risks and uncertainties associated with the automotive supply chain. The benefits of adopting blockchain technology in the automotive supply chain are also described. The developed blockchain-based framework is capable to get more visibility into goods movement and inventory status in automotive supply chains.
Investigating Application and Challenges of Big Data Analytics with Clustering

Very large volumes of data analytics study the uncovering of hidden patterns, interplay, and other discoveries. Today’s technology enables data analyzing and obtaining answers practically quickly- with more conventional solutions for business intelligence, an endeavor that is longer and less effective. The demand for big data analytics has been increasing on regular basis due to the increase in engagement of users. The role of clustering is to make the big data analytic system manageable. This paper has focused on several applications that are based on clustering and big data analytics. The uses of this technology have been increasing rapidly for distance learning, health care, and IoT environment. The issues in the area of clustering and big data are also considered in this research after considering some existing researches in the relevant field. The present research has made use of an advanced mechanism to make dynamic clusters by making use of the K-mean mechanism in order to perform big-data analytics.
Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review

This systematic review aims to provide further insights into the conduct and reporting of clinical prediction model development and validation over time. We focus on assessing the reporting of information necessary to enable external validation by other investigators.We searched Embase, Medline, Web-of-Science, Cochrane Library, and Google Scholar to identify studies that developed 1 or more multivariable prognostic prediction models using electronic health record (EHR) data published in the period 2009-2019.We identified 422 studies that developed a total of 579 clinical prediction models using EHR data. We observed a steep increase over the years in the number of developed models. The percentage of models externally validated in the same paper remained at around 10%. Throughout 2009-2019, for both the target population and the outcome definitions, code lists were provided for less than 20% of the models. For about half of the models that were developed using regression analysis, the final model was not completely presented.Overall, we observed limited improvement over time in the conduct and reporting of clinical prediction model development and validation. In particular, the prediction problem definition was often not clearly reported, and the final model was often not completely presented.Improvement in the reporting of information necessary to enable external validation by other investigators is still urgently needed to increase clinical adoption of developed models.
A learned query rewrite system using Monte Carlo tree search

Query rewrite transforms a SQL query into an equivalent one but with higher performance. However, SQL rewrite is an NP-hard problem, and existing approaches adopt heuristics to rewrite the queries. These heuristics have two main limitations. First, the order of applying different rewrite rules significantly affects the query performance. However, the search space of all possible rewrite orders grows exponentially with the number of query operators and rules and it is rather hard to find the optimal rewrite order. Existing methods apply a pre-defined order to rewrite queries and will fall in a local optimum. Second, different rewrite rules have different benefits for different queries. Existing methods work on single plans but cannot effectively estimate the benefits of rewriting a query. To address these challenges, we propose a policy tree based query rewrite framework, where the root is the input query and each node is a rewritten query from its parent. We aim to explore the tree nodes in the policy tree to find the optimal rewrite query. We propose to use Monte Carlo Tree Search to explore the policy tree, which navigates the policy tree to efficiently get the optimal node. Moreover, we propose a learning-based model to estimate the expected performance improvement of each rewritten query, which guides the tree search more accurately. We also propose a parallel algorithm that can explore the tree search in parallel in order to improve the performance. Experimental results showed that our method significantly outperformed existing approaches.
Social Interaction With Agents and Avatars in Immersive Virtual Environments: A Survey

Immersive virtual reality technologies are used in a wide range of fields such as training, education, health, and research. Many of these applications include virtual humans that are classified into avatars and agents. An overview of the applications and the advantages of immersive virtual reality and virtual humans is presented in this survey, as well as the basic concepts and terminology. To be effective, many virtual reality applications require that the users perceive and react socially to the virtual humans in a realistic manner. Numerous studies show that people can react socially to virtual humans; however, this is not always the case. This survey provides an overview of the main findings regarding the factors affecting the social interaction with virtual humans within immersive virtual environments. Finally, this survey highlights the need for further research that can lead to a better understanding of human–virtual human interaction.
An efficient CNN-LSTM model for sentiment detection in #BlackLivesMatter

Imagining things without mixed emotions is next to impossible in today’s scenario. Whether it is news or any online movement started on social media applications. One of the social media applications i.e Twitter started a movement known as #BlackLivesMatter. The people from all over the world participated showing mixed reactions, sentiments, and emotions such as trusting the movement, gave negative feedback, felt disgusting, showing anger, etc. In this study, a deep learning classifier Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) is used to detect the sentiments and emotions of the people based on the tweets of the two provinces of the USA (Minnesota and Washington D.C.). The proposed hybrid model is validated over Random Forest, Convolutional Neural Network, Long Short Term Memory, and Bidirectional Long Short Term Memory. It is really surprising to see the results as in both the provinces people showing interest as they are trusting the movement with 48% in Minnesota and 54% in Washington D.C. Our proposed model CNN-LSTM is 94% accurate in detecting the various sentiments based on the hyper-parameters such as epoch, filter size, pooling, activation function, dropout, stride, padding, and number of filters.
3D Face Reconstruction in Deep Learning Era: A Survey

3D face reconstruction is the most captivating topic in biometrics with the advent of deep learning and readily available graphical processing units. This paper explores the various aspects of 3D face reconstruction techniques. Five techniques have been discussed, namely, deep learning, epipolar geometry, one-shot learning, 3D morphable model, and shape from shading methods. This paper provides an in-depth analysis of 3D face reconstruction using deep learning techniques. The performance analysis of different face reconstruction techniques has been discussed in terms of software, hardware, pros and cons. The challenges and future scope of 3d face reconstruction techniques have also been discussed.
Review of ASIC accelerators for deep neural network

Deep neural networks (DNNs) have become an essential tool in artificial intelligence, with a wide range of applications such as computer vision, medical diagnosis, security, robotics, and autonomous vehicle. The DNNs deliver the state-of-the-art performance in many applications. The complexity of the DNN models generally increases with application complexity and deployment of complex DNN models requires high computational power. General-purpose processors are unable to process complex DNNs within the required throughput, latency, and power budget. Therefore, domain-specific hardware accelerators are required to provide high computational resources with superior energy efficiency and throughput within a small chip area. In this paper, existing DNN hardware accelerators are reviewed and classified based on the optimization techniques used in their implementations. Each optimization technique generally improves one or more specific performance parameter(s). For example, the hardware optimized for sparse DNNs may provide poor performance for dense DNNs in terms of power and throughput. Therefore, understanding the tradeoff between different hardware accelerators helps to identify the best accelerator model for application deployment. We identify three major areas, ALU, dataflow, and sparsity, in hardware architectures having the potential to improve the overall performance of an accelerator. Existing hardware accelerators for inference are broadly classified into these three categories. As there is no standard model or performance metrics to evaluate the efficiency of the new DNN hardwares in the literature, the classification model can help to identify appropriate performance parameters and benchmark accelerators.
Randomized Balanced Grey Wolf Optimizer (RBGWO) for solving real life optimization problems

Grey Wolf Optimizer (GWO) is one of the most important Swarm Intelligence based meta-heuristic algorithm which follows leadership mechanism and well planned hunting strategies among wolves’ hierarchy. This paper has introduced a new variant of GWO termed as Randomized Balanced Grey Wolf Optimizer (RBGWO), which assists wolves to explore the search space in an efficient manner. The proposed algorithm improves the overall efficiency of the search process by establishing a balance between its exploitation and exploration capability incorporating three successive enhancement strategies equipped with social hierarchy mechanism and random walk with student’s t-distributed random numbers. This newly proposed variant RBGWO has outperformed GWO and its other variants (RW-GWO, EGWO+ and EGWO∗) in most of the cases on CEC 2014 benchmark functions with different scales. Results of the proposed variant have also been verified with the other meta-heuristic algorithms like GSA, CS, TPHS, CL-PSO, LX-BBO, B-BBO, SOS, DERand1Bin, Firefly, GWO, RW-GWO, EGWO+ and EGWO∗ on CEC 2014 benchmark functions. The statistical analysis of the results presents the efficiency of RBGWO (the proposed version) in overall performance. The state-of-the-art methods and the proposed algorithm have also been applied together to constrained and unconstrained real life problems. The results produced by the proposed variant are of better quality compared to that of others in these real-life problems also.
Evaluating pointwise reliability of machine learning prediction

Interest in Machine Learning applications to tackle clinical and biological problems is increasing. This is driven by promising results reported in many research papers, the increasing number of AI-based software products, and by the general interest in Artificial Intelligence to solve complex problems. It is therefore of importance to improve the quality of machine learning output and add safeguards to support their adoption. In addition to regulatory and logistical strategies, a crucial aspect is to detect when a Machine Learning model is not able to generalize to new unseen instances, which may originate from a population distant to that of the training population or from an under-represented subpopulation. As a result, the prediction of the machine learning model for these instances may be often wrong, given that the model is applied outside its "reliable" space of work, leading to a decreasing trust of the final users, such as clinicians. For this reason, when a model is deployed in practice, it would be important to advise users when the model's predictions may be unreliable, especially in high-stakes applications, including those in healthcare. Yet, reliability assessment of each machine learning prediction is still poorly addressed. Here, we review approaches that can support the identification of unreliable predictions, we harmonize the notation and terminology of relevant concepts, and we highlight and extend possible interrelationships and overlap among concepts. We then demonstrate, on simulated and real data for ICU in-hospital death prediction, a possible integrative framework for the identification of reliable and unreliable predictions. To do so, our proposed approach implements two complementary principles, namely the density principle and the local fit principle. The density principle verifies that the instance we want to evaluate is similar to the training set. The local fit principle verifies that the trained model performs well on training subsets that are more similar to the instance under evaluation. Our work can contribute to consolidating work in machine learning especially in medicine.
Single-Stage UAV Detection and Classification with YOLOV5: Mosaic Data Augmentation and PANet

In Drone-vs-Bird Detection Challenge in conjunction with the 4th International Workshop on Small-Drone Surveillance, Detection and Counteraction Techniques at IEEE AVSS 2021, we proposed a YOLOV5-based object detection model for small UAV detection and classification. YOLOV5 leverages PANet neck and mosaic augmentation which help in improving detection of small objects. We have combined the challenge dataset with one of the publicly available UAV air to air dataset having complex background and lighting conditions for training the model. The proposed approach achieved 0.96 Recall, $0.98 mAP_{0.5}$, and $0.71 mAP_{0.5:0.95}$ on the 10% randomly sampled dataset from the whole dataset.
A designated cloud server-based multi-user certificateless public key authenticated encryption with conjunctive keyword search against IKGA

With the massive growth of the World Wide Web (i.e., internet), individual users or companies can store and manage large amounts of sensitive data to outsource it to the cloud server. Despite its advantages, such as reducing local storage, reducing overhead, and providing backups, it faces security and data privacy issues. Many public-key encryption with keyword search (PEKS) schemes are developed to resolve the above problems. Nevertheless, those are not efficiently suitable for multi-user scenario applications. Recently, a new scheme developed called Multi-user certificateless public key encryption with a conjunctive keyword search (mCLPECK) scheme. It contains two features: the multi-user environment and the conjunctive keyword search function. Nevertheless, it requires a secure channel while transmitting the trapdoor to the cloud server, which is not practical in applications, and it cannot resist the keyword guessing attack (KGA). We propose a primitive called a designated cloud server-based multi-user certificateless public key authenticated encryption with conjunctive keyword search (dmCLPAECKS) scheme. It doesn’t require a secure channel between the designated cloud server and a data receiver and provides security against keyword guessing attack (KGA) in random oracle model. Furthermore, our proposed scheme shows better results than other related schemes in terms of communication and computation cost.