Reviewerly/exHarmony huggingface dataset:
Content Based Image Retrieval Based on Feature Fusion and Support Vector Machine

Large number of image datasets have been generated with diverse functionality and applications. This is because of the development in communication technologies and affordable image acquisition devices. However, the generated image datasets require an efficient search engine to retrieve images that meet the needs of end-user. Content-Based Image Retrieval (CBIR) is an automatic mechanism to retrieve images from relevant class according to their visual content. In this paper, local and global features are combined to provide a powerful image descriptor. CBIR performance is measured through two major key factors which are: accuracy and retrieval time. The first one is related to the amount of retrieved images from the same semantic class while the latter is related to the speed of the search. There is a tradeoff between these two factors. In this regard, this paper proposes CBIR algorithm based on feature fusion and support vector machine (SVM). The feature are texture, shape, and color features. The statistical moments, mean and standard deviation are calculated after transforming the RGB channels to moments' domain through the use of SKTP to construct the color descriptor. Canny Edge Detection and LBP are utilized to construct edge and texture descriptors, respectively. WANG dataset is used to assess the performance of the proposed algorithms. The proposed algorithm focused on achieving an interesting accuracy level (90.15 %) through the use of SVM classifier. Thus, the proposed algorithm achieved its goal and outperformed existing algorithms.
Efficient Email phishing detection using Machine learning

Emails are frequently utilized as a way of personal and professional communication. Banking information, credit reports, login data, and other sensitive and personal information are frequently transmitted over email. This makes them valuable to cyber criminals, who can exploit the knowledge for their own gain. Phishing is a technique used by con artists to steal sensitive information from people by impersonating well-known sources. The sender of a phished email can persuade you to disclose personal information under false pretenses. The detection of a phished email is treated as a classification problem in this research, and this paper shows how machine learning methods are used to categorize emails as phished or not. LMT classifiers attain a maximum accuracy in email classification.
FPGA based Histogram Equalization for Image Processing

Histogram Equalization is one of the most common algorithms used in image processing applications. Traditional software based histogram equalization using CPU is no longer sufficient for high-computation based real-time image processing applications. To meet high computation demand, image processing operations can be hardware accelerated using Field Programmable Gate Array (FPGA). In this paper, we implemented histogram equalization using Verilog Hardware Description Language (HDL) on Xilinx FPGA for hardware acceleration. The result shows that our FPGA-based histogram equalization is on average 1.5 thousand times faster than traditional software based histogram equalization on CPU.
Secure and efficient privacy protection system for medical records

The healthcare sector has witnessed robust growth in the realm of medical information and its application during the last two decades. Consequently, the universal availability of patient records using web-based technology has acquired great significance in providing quality healthcare services on a global scale. With paramount aspects such as medical records confidentiality, it is an important consideration of modern times to secure medical information like CT-scan, X-Ray, and MRI when transmitted across public networks. Combining watermarking and BioHashing in medical image protection provides the means to safely transmit data over the internet and enhance authentication. In this context, we present a BioHashing and watermarking-based technique capable of providing integrity, authenticity, and confidentiality to different medical images. At the sender side, first, the BioHashing method is used to generate the watermark which is then embedded in weber local descriptor excitation component images using discrete cosine transform. The proposed technique meets all the requirements of robustness and imperceptibility. The experimental and comparison results demonstrate that the proposed method is superior to existing methods.
Tribe or Not? Critical Inspection of Group Differences Using TribalGram

With the rise of AI and data mining techniques, group profiling and group-level analysis have been increasingly used in many domains, including policy making and direct marketing. In some cases, the statistics extracted from data may provide insights to a group’s shared characteristics; in others, the group-level analysis can lead to problems, including stereotyping and systematic oppression. How can analytic tools facilitate a more conscientious process in group analysis? In this work, we identify a set of accountable group analytics design guidelines to explicate the needs for group differentiation and preventing overgeneralization of a group. Following the design guidelines, we develop TribalGram , a visual analytic suite that leverages interpretable machine learning algorithms and visualization to offer inference assessment, model explanation, data corroboration, and sense-making. Through the interviews with domain experts, we showcase how our design and tools can bring a richer understanding of “groups” mined from the data.
Oncological Applications of Deep Learning Generative Adversarial Networks

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Transactions across serverless functions leveraging stateful dataflows

Serverless computing is currently the fastest-growing cloud services segment. The most prominent serverless offering is Function-as-a-Service (FaaS), where users write functions and the cloud automates deployment, maintenance, and scalability. Although FaaS is a good fit for executing stateless functions, it does not adequately support stateful constructs like microservices and scalable, low-latency cloud applications. Recently, there have been multiple attempts to add first-class support for state in FaaS systems, such as Microsoft Orleans, Azure Durable Functions, or Beldi. These approaches execute business code inside stateless functions, handing over their state to an external database. In contrast, approaches such as Apache Flink's StateFun follow a different design: a dataflow system such as Apache Flink handles all state management, messaging, and checkpointing by executing a stateful dataflow graph providing exactly-once state processing guarantees. This design relieves programmers from having to "pollute" their business logic with distributed systems error checking, management, and mitigation. Although programmers can easily develop applications without worrying about messaging and state management, executing transactions across stateful functions remains an open problem. In this paper, we introduce a programming model and implementation for transaction orchestration of stateful serverless functions. Our programming model supports serializable distributed transactions with two-phase commit, as well as eventually consistent workflows with Sagas. We design and implement our programming model on Apache Flink StateFun to leverage Flink's exactly-once processing and state management guarantees. Our experiments show that the approach of building transactional systems on top of dataflow graphs can achieve very high throughput, but with latency overhead due to checkpointing mechanism guaranteeing the exactly-once processing. We compare our approach to Beldi that implements two-phase commit on AWS lambda functions backed by DynamoDB for state management, as well as an implementation of a system that makes use of CockroachDB as its backend.
Research on Cross-chain Technology of Blockchain: Challenges and Prospects

As one of the core technologies of blockchain, cross-chain technology has been widely valued by academia and industry, and has made a series of research achievements in recent years. At present, the overview research on cross-chain technology generally compares and analyzes cross-chain technology as a whole, and lacks the decoupling comparison of the main steps in cross-chain technology. Firstly, the basic principle of cross-chain technology is summarized and analyzed. Then, the cross-chain technology is divided into two types: isomerism and isomorphism, and the existing problems and corresponding solutions are analyzed and compared. Finally, the development status and trend of cross-chain technology are summarized, as well as important research directions in the future.
Artificial Intelligence in Pediatric Pathology: The Extinction of a Medical Profession or the Key to a Bright Future?

Artificial Intelligence (AI) has become of increasing interest over the past decade. While digital image analysis (DIA) is already being used in radiology, it is still in its infancy in pathology. One of the reasons is that large-scale digitization of glass slides has only recently become available. With the advent of digital slide scanners, that digitize glass slides into whole slide images, many labs are now in a transition phase towards digital pathology. However, only few departments worldwide are currently fully digital. Digital pathology provides the ability to annotate large datasets and train computers to develop and validate robust algorithms, similar to radiology. In this opinionated overview, we will give a brief introduction into AI in pathology, discuss the potential positive and negative implications and speculate about the future role of AI in the field of pediatric pathology.
Performance and scalability evaluation of a permissioned Blockchain based on the Hyperledger Fabric, Sawtooth and Iroha

This paper shows the performance and scalability evaluation of different blockchain platform implementations. Hyperledger Iroha implementing YAC consensus, Sawtooth implementing PoET algorithm, and Hyperledger Fabric framework implementation. Performance evaluation and scalability assessment were done by varying different sets of parameters such as block size, transaction sending rate, network traffic distribution, and network size. Performance evaluation was done based on average transaction latency, network throughput, and transaction failure rate. Scalability was assessed based on changes in transaction latency and throughput with increasing network size. Test results let to study the impact of a particular parameter on the private blockchain network performance and show how they can be adjusted to improve performance.
Calibration-Net:LiDAR and Camera Auto-Calibration using Cost Volume and Convolutional Neural Network

A fusion of multi-sensor has been utilized widely for improving the environment perception in autonomous vehicles and robot navigation. Calibration is an essential procedure for preprocessing the data fusion between multiple sensors. Most target-based calibration techniques require manual works and specific calibration targets to achieve high accuracy. It gradually becomes outmoded for Light Detection and Ranging (LiDAR) and camera with the development of deep learning techniques. This paper proposed an online LiDAR-camera calibration that automatically predicts the extrinsic parameters by taking advantage of convolutional neural networks (CNNs). We take depth maps of stereo camera prediction and depth maps of the LiDAR projection as two separated branches as inputs for the proposed network. Unlike the current CNN-based calibration method, we construct a cost volume of the correlation between two corresponding pixels of depth maps in stereo camera and LiDAR, respectively. The proposed model gains a reasonable capability to adjust to different initial calibration error ranges. We evaluate the proposed architecture on the KITTI dataset and achieve 0.378 degree in rotation error and 2.353cm translation error.
Deep Learning-based Action Recognition for Pedestrian Indoor Localization using Smartphone Inertial Sensors

For decades, the identification of human action has been one of the most critical study areas in artificial intelligence research. The study in human activity recognition aims to classify people's behavior based on data that is already accessible, such as pictures, visual, sensing, or stream recorded data, among other things. A common distinction made by most human activity recognition methods is between daily tasks such as walking and specific behaviors like standing or sitting. More specialized functions targeted to the use-case of pedestrian interior navigation are investigated in this study. These activities include several types of stationary and movement activities and various other activities. A deep learning-based human activity recognition framework is presented that can be used to recognize people's actions inside an indoor localization situation by analyzing smartphone inertial sensor data. Furthermore, we introduced a 1D-ResNet-SE model in the framework and assessed it using a publicly available dataset known as the Activity Logs dataset. According to the experimental measurements, the 1D-ResNet-SE surpasses other standard deep learning techniques in terms of accuracy, with a maximum accuracy of 84.11% in the general scenario.
Profiling temporal learning interests with time-aware transformers and knowledge graph for online course recommendation

Profiling users’ temporal learning interests is key to online course recommendation. Previous studies mainly profile users’ learning interests by aggregating their historical behaviors with simple fusing strategies, which fails to capture their temporal interest patterns underlying the sequential user behaviors. To fill the gap, we devise a recommender that incorporates time-aware Transformers and a knowledge graph to better capture users’ temporal learning interests. First, we introduce stacked Transformers to extract users’ temporal learning interests underlying users’ course enrollment sequences. In addition, we design a time-aware positional encoding module to consider the enrollment time intervals between courses. Third, we incorporate a knowledge graph to utilize the latent knowledge connections between courses. The proposed method outperforms state-of-the-art baselines for course recommendation. Furthermore, findings in the ablation study offers several insights for future research. The proposed model can be implemented in online learning platforms to increase user engagement and reduce dropout rate.
Human and action recognition using adaptive energy images

In this paper, we propose a new temporal template approach for action recognition and person identification based on motion sequence information in masked depth video streams obtained from RGB-D data. This new representation creates a membership function that models the change in motion based on the correlation between frames that occur during motion flow. The energy images created with this function emphasize the intervals of motion with more change, while the intervals with less change are suppressed. To understand the distinctive features, the obtained energy images by using the proposed function are given as input to the convolutional neural networks and different handcrafted classifiers. The proposed method was observed on the BodyLogin, NATOPS, and SBU Kinect datasets and compared with the existing temporal templates and recent methods. The results indicate that the proposed method provides both higher performance and better motion representation.
A high-applicability heterogeneous cloud data centers resource management algorithm based on trusted virtual machine migration

With the continuous development and maturity of cloud computing technology, the scale and number of cloud data center (CDC) are also expanding. This increasingly draws attention to the problem of high energy consumption in CDCs. Dynamic virtual machine (VM) consolidation is a promising approach for reducing energy consumption. VM migration, as a VM consolidation technology, can effectively improve the utilization of physical machine (PM) and optimize the scheduling process of CDCs. However, most VM integration algorithms, in existing research, are aimed at improving the utilization of PMs. Excessive utilization of PMs may increase the competition for shared resources among the VMs running on them. As a result, the performance of these VMs deteriorates, and the execution time of cloud tasks is increased or even interrupted. This study systematically analyzes the overall architecture of CDCs. Subsequently, migration rules are established for the one-dimensional and multidimensional trusted VMs. A high- applicability heterogeneous CDC resource management algorithm based on trusted VM migration (HTVM2) is then proposed. The proposed algorithm not only solves the one-dimensional VM migration problem of homogeneous and heterogeneous CDCs but also those of multi-dimensional VMs. This improves the success rate of VM migration, reduces the energy consumption of the CDC, and improves load balancing while ensuring VM performance. Finally, the algorithm was compared with the other three algorithms outperforming them all, as demonstrated by experimental results.
Simulation of SisFall Dataset for Fall Detection Using MATLAB Classifier Algorithms

Fall accidents are considered one of the significant global public health concerns, and the largest proportion of fatal accidents are experienced by elderly people aged 65 and above. Currently, there is a demand for creating an effective machine learning-based fall detection system that is significantly portable at a low cost. For development, public datasets are available to simulate an effective classifier. Hence, the current study was aimed to simulate the SisFall dataset to acquire an effective machine learning classifier for fall detection. The methodology included a study of the various fall detection systems as well as general features for machine learning classifications. The most suitable potential combination of machine learning algorithms that will provide the best accuracy, precision, sensitivity, specificity, and lowest training time was developed via simulation models using MATLAB. Input data was selected from the SisFall dataset for simulations. Up to 24 algorithms, including Decision Trees, Discriminant Analysis, Naïve Bayes, Support Vector Machines (SVM), k-Nearest Neighbors (KNN) and available Ensemble Classifiers, were simulated. Four sets of experiments were done using accelerometers with varying features and cross-validation. Currently, a combination of Cubic SVM and Fine KNN was chosen to be used as the most appropriate classifier to train a fall detection system.
Analysis of Explainable Goal-Driven Reinforcement Learning in a Continuous Simulated Environment

Currently, artificial intelligence is in an important period of growth. Due to the technology boom, it is now possible to solve problems that could not be resolved previously. For example, through goal-driven learning, it is possible that intelligent machines or agents may be able to perform tasks without human intervention. However, this also leads to the problem of understanding the agent’s decision making. Therefore, explainable goal-driven learning attempts to eliminate this gap. This work focuses on the adaptability of two explainability methods in continuous environments. The methods based on learning and introspection proposed a probability value for success to explain the agent’s behavior. These had already been tested in discrete environments. The continuous environment used in this study is the car-racing problem. This is a simulated car racing game that forms part of the Python Open AI Gym Library. The agents in this environment were trained with the Deep Q-Network algorithm, and in parallel the explainability methods were implemented. This research included a proposal for carrying out the adaptation and implementation of these methods in continuous states. The adaptation of the learning method produced major changes, implemented through an artificial neural network. The obtained probabilities of both methods were consistent throughout the experiments. The probability result was greater in the learning method. In terms of computational resources, the introspection method was slightly better than its counterpart.
A method for reducing cloud service request peaks based on game theory

The rapid development of Internet of Things (IoT) technology and the popularity of Artificial Intelligence (Al) technology research have brought new opportunities for the development of cloud computing (CC). With the increasing number of mobile Internet access devices and IoT access devices, the number of task requests from CC customers for AI services in the network has also experienced an explosive growth. In this paper, the focus is on the possible overload of cloud providers during the peak period of cloud service requests. Thetime attributes of cloud task execution are classified to avoid overloading the cloud provider as much as possible. In a distributed cloud environment, it is necessary to consider the time flexible attributes of cloud tasks to reasonably compete for cloud resources. In this work, game theory (GT) is introduced to formulate a cloud service scheduling game, in which participants are cloud customers who participate in the purchase of cloud services. The players' strategies are the time flexibility of each cloud task. The problem is formulated as minimizing the cost of scheduling cloud services and a noncooperative game among the customers (as players) is presented. Then the existence of the Nash equilibrium (NE) solution of the game has been proved and a new algorithm has been proposed in this paper to compute it. In addition, the analysis process of the convergence of the proposed PCA algorithm and the proof of its convergence to NE are also included in this paper. At the end of the paper, simulations were performed to verify the theoretical analysis presented. The experimental results show that the proposed PCA algorithm can converge to the Nash equilibrium very quickly, effectively reducing the peak value and increasing profit.