Reviewerly/exHarmony huggingface dataset:
Feature Selection of the Rich Model Based on the Correlation of Feature Components

Currently, the popular Rich Model steganalysis features usually contain a large number of redundant feature components which may bring “curse of dimensionality” and large computation cost, but the existing feature selection methods are difficult to effectively reduce the dimensionality when there are many strongly correlated effective feature components. This paper proposes a novel selection method for Rich Model steganalysis features. First, the separability of each feature component in the submodels of Rich Model is measured based on the Fisher criterion, and the feature components are sorted in the descending order based on the separability. Second, the correlation coefficient between any two feature components in each submodel is calculated, and feature selection is performed according to the Fisher value of each component and the correlation coefficients. Finally, the selected submodels are combined as the final steganalysis feature. The results show that the proposed feature selection method can effectively reduce the dimensionalities of JPEG domain and spatial domain Rich Model steganalysis features without affecting the detection accuracies.
Customer Service Effectiveness in Shared Mobility Systems Using Artificial Intelligence Algorithms

Sharing economy has been developing dynamically for a few years. Shared mobility systems are one of the most interesting dynamically developing concepts of the sharing economy. All around the world alternative transport modes have become an object of interest while being promoted as innovative forms of urban mobility. Shared mobility systems are focused on reducing the number of transport vehicles by using each of them more efficiently. In order for these systems to develop, an efficient customer service is needed. Nowadays, artificial intelligence is increasingly reshaping services by performing various tasks in an innovative way. Artificial intelligence algorithms for customer service are pushing the envelope of innovation and revolutionizing the way customers are assisted. Currently, AI-assisted bots can efficiently handle first-level queries and assist in reducing operational costs. Currently, artificial intelligence is used in customer support service in industries such as: finance, retail, travel, food and transport. The aim of the chapter is to create an algorithm dedicated to the customer service in shared mobility system which includes sample dialogs, flow diagram and prompt lists adapted to shared mobility systems.
An Aphoristic Study on Different Interpolation Techniques for Medical Image Scaling and its Comparative Analysis

Image transforms are widely used in Image processing and Image analysis. Image transforms are useful for fast computation of convolution and correlation. Image Scaling refers to the resizing of images in digital image processing. The resolution of images is a major factor when the images are enlarged. The enlarging of images in turn refers to the up sampling, when the image is up sampled, there will be a decrease in the image quality. To improve the quality of the image, different interpolation techniques has been proposed. The main objective of this paper is to study different interpolation techniques for scaling of images such as nearest neighbor, bilinear interpolation and bicubic interpolation and compared in both frequency domain and spatial domain.
Optimal mixed discrete-continuous planning for linear hybrid systems

Planning in hybrid systems with both discrete and continuous control variables is important for dealing with real-world applications such as extra-planetary exploration and multi-vehicle transportation systems. Meanwhile, generating high-quality solutions given certain hybrid planning specifications is crucial to building high-performance hybrid systems. However, since hybrid planning is challenging in general, most methods use greedy search that is guided by various heuristics, which is neither complete nor optimal and often falls into blind search towards an infinite-action plan. In this paper, we present a hybrid automaton planning formalism and propose an optimal approach that encodes this planning problem as a Mixed Integer Linear Program (MILP) by fixing the action number of automaton runs. We also show an extension of our approach for reasoning over temporally concurrent goals. By leveraging an efficient MILP optimizer, our method is able to generate provably optimal solutions for complex mixed discrete-continuous planning problems within a reasonable time. We use several case studies to demonstrate the extraordinary performance of our hybrid planning method and show that it outperforms a state-of-the-art hybrid planner, Scotty, in both efficiency and solution qualities.
Taxonomy extraction using knowledge graph embeddings and hierarchical clustering

While high-quality taxonomies are essential to the Semantic Web, building them for large knowledge graphs is an expensive process. Likewise, creating taxonomies that accurately reflect the content of dynamic knowledge graphs is another challenge. In this paper, we propose a method to automatically extract a taxonomy from knowledge graph embeddings, and evaluate it on DBpedia. Our approach produces a taxonomy by leveraging the type information contained in the graph and the tree-like structure of an unsupervised hierarchical clustering performed over entity embeddings. We then extend our method with an axiom induction mechanism which allows us to identify new classes from the data and describe them with logical axioms, thus leading to expressive taxonomy extraction.
2.5D visual relationship detection

Visual 2.5D perception involves understanding the semantics and geometry of a scene through reasoning about object relationships with respect to the viewer. However, existing works in visual recognition primarily focus on the semantics. To bridge this gap, we study 2.5D visual relationship detection (2.5VRD), in which the goal is to jointly detect objects and predict their relative depth and occlusion relationships. Unlike general VRD, 2.5VRD is egocentric, using the camera’s viewpoint as a common reference for all 2.5D relationships. Unlike depth estimation, 2.5VRD is object-centric and does not only focus on depth. To enable progress on this task, we construct a new dataset consisting of 220K human-annotated 2.5D relationships among 512K objects from 11K images. We analyze this dataset and conduct extensive experiments including benchmarking multiple state-of-the-art VRD models on this task. Experimental results show that existing models largely rely on semantic cues and simple heuristics to solve 2.5VRD, motivating further research on models for 2.5D perception. We will make our dataset and source code publicly available.
Copy-Move Image Forgery detection via Combined Pseudo-Zernike Moment Invariant

Copy-move image forgery (CMIF) is one of the greatest threats in the field of image forensic analysis where a small region of an image is copied and pasted at other positions of the same image. This paper proposes a hybrid copy-move image forgery detection (CMIFD) approach. Initially, the SIFT algorithm is applied to the entire image and the extracted key-points are matched via 2NN process to obtain matched key-points. Furthermore, larger blocks are formed around each matched key-points followed by the computation of Combined Pseudo Zernike moment invariants (CPZMI) for each block. CPZMI feature descriptors of these blocks are simultaneously invariant to rotation, scaling, blurring and robust to noise. Consequently, comparison of CPZMI feature descriptors allows us to obtain stable matched key-points. Our proposed CMIFD technique is validated using benchmark datasets and found to exhibit better performance as compared with existing state-of-the-art techniques.
Cutting Edge IoT Technology for Smart Indian Pharma

Indian pharmaceutical industry has been anticipated best over the years across world. It's a larger supplier of medical facilities to entire world and a leading nation in pharma manufacturing. Internet of things provides the pharma industry with a selection of new personalized market opportunities, better controlled environment of pharma manufacturing, prevent maintenance of equipment and improve diversified supply chain management system. Owing to the deficiencies in transparency, the pharmaceutical manufacturing may be unable to warrant innocuous and safe medicinal production and circulation. Adoption of internet of things could represent a new competitive advantage for pharma companies exhibiting pharma IoT. This is a conceptual explorative research demonstrates future of Indian pharma, which is in a verge to become self-reliant nation. The use of internet of things, artificial intelligence, big data analytics and tracking of customers carrying internet connected digital devices (TCCICDD) and sensors implanted in wearables would help to locate the customer medical history with health monitoring. This would also alert both the clinical manufacturers and users resulting in designing personalized based better future of Indian pharma industry. Future Pharma would see the business link paraphernalia across development and dispersal by using better, more reliable larger volumes of data to revolutionize manufacturing.
Incremental Search Space Construction for Machine Learning Pipeline Synthesis

Automated machine learning (AutoML) aims for constructing machine learning (ML) pipelines automatically. Many studies have investigated efficient methods for algorithm selection and hyperparameter optimization. However, methods for ML pipeline synthesis and optimization considering the impact of complex pipeline structures containing multiple preprocessing and classification algorithms have not been studied thoroughly. In this paper, we propose a data-centric approach based on meta-features for pipeline construction and hyperparameter optimization inspired by human behavior. By expanding the pipeline search space incrementally in combination with meta-features of intermediate data sets, we are able to prune the pipeline structure search space efficiently. Consequently, flexible and data set specific ML pipelines can be constructed. We prove the effectiveness and competitiveness of our approach on \(28\) data sets used in well-established AutoML benchmarks in comparison with state-of-the-art AutoML frameworks.
Research on Person Reidentification Method Fusing Direction Information in Multi-camera Pedestrian Tracking Problem

Abstract In multi-camera pedestrian tracking, pedestrian apparent features are usually used to solve the problem of cross-camera reidentification. In order to make the pedestrian apparent features robust to the directional changes of pedestrians in different cameras. In this paper, a pedestrian feature extraction method combining directional features is proposed by using deep network Resnet 50. Pedestrian feature extraction network is trained by adding pedestrian direction information. in order to improve the performance of person reidentification model, BNblock structure is added in the design, a batch normalization (BN) layer is added after the feature to get the normalized feature. It can make the triplet loss converge with the convergence of the ID loss, thereby improving the performance of the model. The proposed method was verified on DukeMTMC and Market-1501 data sets, and the results show that the pedestrian appearance features combined with direction information proposed by the pedestrian reidentification algorithm can significantly improve the reidentification accuracy. By adding BNblock into the network structure, the accuracy can be further improved.
FaceIgnition: An automatic anti-theft and keyless solution for vehicles

The automobile industry is a sector that brings together art, innovation, science, technology and ideas that empower the economy, people and the nation as a whole. A great deal of advancement has been done in automobiles, but there is always room for improvement. Security of the vehicle is an essential requirement. The conventional key-turning ignition process has become a hassle as access to the vehicle requires the user to possess the key at all times. In a scenario where the key is unavailable, it becomes very difficult to make use of the vehicle. So, In today's fast-paced technological world, it becomes imperative that a keyless system be built, that would provide the user with ease to operate the vehicle. Considering this, we have developed "FaceIgnition", which is an automatic vehicle ignition system that makes use of face recognition to check authenticity along with providing security against theft and unauthenticated use. It enables the user to make use of the vehicle without having the need to keep the key.
A New Approach Based on Fuzzy-Adaptive Structure &amp; Parameter Learning Applied in Meta-Cognitive Algorithm for ANFIS

A fuzzy inference system (FIS) is a widely used modeling and simulating approach based on the concept of fuzzy set theory, fuzzy if-then rules and fuzzy logic systems. All fuzzy inference systems have an adaptive composition that parameters are set during the training process. There is not any especial instruction to change the training parameters or the structure of the fuzzy inference systems during the training process. In this paper, a new online approach is proposed to adapt some systematic parameters of a typical fuzzy inference system. Employing this approach, some training and systematic parameters are selected during the training procedure to be constant or to be adaptive. This approach is realized and examined on the Adaptive Neural-fuzzy Inference System (ANFIS) and Meta-cognitive neuro fuzzy inference system (McFIS). The training process of the McFIS consists of two portions which includes a cognitive and a meta-cognitive one. A training mechanism controls the learning process of the cognitive portion. This paper illustrates the meta-cognitive training algorithm for parameterization of the fuzzy inference systems and applies the proposed approach for functional and systematic or structural parameter selection in the meta-cognitive algorithm (McFIS) which ends to some noticeable merits. The idea is examined by numerical examples and the results are compared with other methods. It is shown that the proposed approach reveals acceptable and efficient performance in the structure selection problem.
Question answering system based on tourism knowledge graph

Abstract Nowadays tourism information services only provide users with massive and fragmented information returned by independent network search which makes users often need to spend a lot of time and energy to find what they really want from the massive data. As a result, route designing is very complicated. In view of this situation, this study builds a tourism knowledge graph based on neo4j and constructs a question answering system (QA). Also, we carry out the model and system performance evaluation, trying to improve the user satisfaction with query experience. According to the structure of question answering system (QA), this research designed and implemented named entity recognition (NER) model based on Bert-BiLSTM-CRF and matching reasoning model based on templates. With the above methods, natural language questions were successfully transformed into cypher query statements recognizable in graph database, and the corresponding answers will be captured and returned from tourism knowledge graph. According to the experiment, the method of Bert-BiLSTM-CRF obtains the state of art and QA system performs quickly and efficiently. For the purpose that artificial intelligence helps the development of tourism industry, this study has a certain significance.
Privacy-Aware Key Task Scheduling in Vehicular Networks Based on Incentive Mechanism

The introduction of a lightweight encryption scheme in the vehicular network has improved the reliability and security of data transmission substantially among vehicles. However, it is still not guaranteed that all vehicles comply with the encryption scheme throughout the whole operation period, considering the inherent selfishness of each participant. Apart from that, the data scheduling issue is another major concern in the vehicular network due to the high-speed mobility of vehicles. To tackle these issues, a multi-objective and multi-dimensional incentive mechanism is developed in this paper to achieve privacy-aware data scheduling for vehicles. This mechanism is designed to encourage vehicles to carry out different tasks by providing relevant incentives while maximizing the overall utility of the network. Additionally, security and privacy of data transmission between vehicles and cloud servers are realized through a data perturbation approach. Experimental evaluation shows that the proposed incentive mechanism is better than the traditional methods when it comes to maximizing the number of participants and completed key tasks.
Providing Resilience on Cloud Computing

In Cloud Computing, a wide range of virtual platforms are integrated and offer users a flexible pay-as-you-need service. Compared to conventional computing systems, the provision of an acceptable degree of resilience to cloud services is a daunting challenge due to the complexities of the cloud environment and the need for efficient technology that could sustain cloud advantages over other technologies. For a cloud guest resilience service solution, we provide architectural design, installation specifics, and performance outcomes throughout this article. Virtual Machine Manager (VMM) enables execution statistical test of the virtual machine states to be monitored and avoids to reach faulty states.
A Mobile Application (App) Based on Realistic Mathematics Education

Persistent poor results in mathematics can be ascribed to the content being taught so that it is removed from reality. This chapter reports on the design of a mobile application based on the realistic mathematics education approach. A purposeful stratified sample yielded participants from a group of in-service teachers enrolled for a post-graduate degree in mathematics education. A qualitative design-based research method was followed. The research produced a mobile application which serves as an intervention tool for practicing mathematics teachers to make the content more relevant and realistic. Various design principles were also formulated that could assist in future mobile app design projects. The mobile application can be used in a blended learning context, especially in underprivileged communities since accessibility to mobile devices is common. The availability of the app for free on Google Play store has the potential to assist mathematics teachers and learners from any context to improve their achievement in mathematics.
Evaluating the Malware Threat Cases in Fiji 2020

Cyber risk was regarded as an unusual operating risk for the organization through networks (e.g., the internet) and software use. This view has developed over time to incorporate operating threats relevant to the organization and immediate companies. In the Pacific zone, cybersecurity is an ever-growing issue. Thus, in this battle, the solutions became only a key cyber defense to hurdle in the quickly changing cybersecurity technology. Therefore, this danger is augmented by the fact that cyber safety remains a fairly new field with a significant shortage of technology and expertise. Very few countries have placed national cyber defense policies into motion within our Pacific region, and not many companies can deal with ever-changing threats in addition to regulations. For that reason, keeping safety in mind for Fiji, our research remarks an emphasis in detail on the trend and traces of various threats taking place in 2020.
Development of Medical Image Encryption System Using Byte-Level Base-64 Encoding and AES Encryption Method

Safety is essential in general. Some data requires security to be protected from unwanted things. One way to improve security is to implement encryption. This encryption method is relatively fast compared to another encryption. The algorithm used to perform this encryption is the Advanced Encryption Standard. This thesis discusses the development of medical image encryption systems. The histogram is used to compare the distribution of values and the number of pixels of the original image with the image after processing, as well as the correlation coefficient to determine the correlation between pixels in the image. From the tests that have been done, the Medical Image Encryption System Using the AES Encryption Method will produce a randomized new image with an average RMS Error of 4388.39 and the average value for the horizontal correlation coefficient of 0.03344, vertical of 0.00742 and diagonal of 0.01110.
The next best question

Many real-world prediction tasks are hampered by the problem of having limited knowledge available for use in making predictions. Additional knowledge can often be acquired with an investment of resources, however there is great interest in minimizing the resources invested in the process of data gathering, without compromising the quality of prediction. In this paper, we present a framework for adaptive feature acquisition, comprised of three replaceable components, which tackles this problem. At the core of our method is the search for the next best question (TNBQ) to ask, given the data currently available, in order to optimally acquire features. We evaluate the framework using various datasets and provide an analysis of its performance with different configurations. We also demonstrate the benefits of the proposed framework and compare it to the state-of-the-art method.
Investigation of DNN Model Robustness Using Heterogeneous Datasets

Deep learning frameworks have been successfully applied to tackle many challenging tasks in pattern recognition and computer vision thanks to its ability to automatically extract representative features from the training data. Such type of data-driven approach, however, is subject to the criticism of too much dependency on the training set. In this research, we attempt to investigate the validity of this statement: `deep learning is only as good as its data' by evaluating the performance of deep learning models using heterogeneous data sets, in which distinct representations of the same source data are employed for training/testing. We have examined three cases: low-resolution image, severely compressed input and halftone image in this work. Our preliminary results indicate that such dependency indeed exists. Classifier performance drops considerably when the model is tested with modified or transformed input. The best outcomes are obtained when the model is trained with hybrid input.