Reviewerly/exHarmony huggingface dataset:
Variability - Surface intensity distribution of large-area reference sources

In this paper we present the impact of variability, a surface source parameter, on the efficiency evaluation of surface contamination monitors. This study was based on two source uniformity correction methodologies and data from real surface source distributions. Surface source intensity distribution has been changed by rearranging the cells (portions of the active area of each LARS) while keeping the same source uniformity value. Instrument efficiencies have been calculated for different sets of uniformities and variabilities. This study led to emphasize the importance of variability, a differential source intensity distribution parameter, over the uniformity, an integral source intensity distribution parameter, and reinforced the importance of the source uniformity correction procedure on the course of surface contamination monitor calibration.
A Hybrid Approach for Credit Card Fraud Detection Using Naive Bayes and Voting Classifier

The prediction analysis is the approach which is applied to predict future possibilities from the current data. One need not pay cash, the cardholder just gives his card to the shopkeeper, he will swipe the card and the payment will be done in just fractions of a second. Sometimes the credit card can be stolen, and then the attacker can make false transactions. In this type of conditions, the credit card company faces a huge loss. In the existing research work, the voting-based classification approach is applied for credit card fraud detection. The voting based classification is a combination of multiple classifiers like SVM, decision tree etc. The classifier will have maximum accuracy will display its predicted result. To improve the accuracy of prediction analysis the voting based classification method will be replaced with naïve bayes classification approach. The naïve bayes classifier is the probability based classifier for credit card fraud detection. In the probability based classification method, the probabilities of the target classes are calculated and the probability of the test data is calculated. The test set which is near to the probability class is identified as the target set. The naïve bayes classification approach will improve the accuracy of credit card fraud detection. The proposed methodology will be implemented in python and results will be analyzed in terms of accuracy, precision, recall and F-measure. The naïve Bayes classifier optimizes the results in terms of accuracy, precision, recall and f-measure is optimized up to 10 to 15% for the credit card fraud detection.
Efficient Model for Searching and Detecting Semantically Similar Question in Discussion Forums of e-learning Platforms

Udemy and Nptel are online learning platform where learners get access to wide variety of courses. These platforms have 2 issues which we aim to solve. First, Currently in Udemy the questions asked in discussion forum are not divided based on course videos, i.e. there is common discussion forum where question with respect to any content in the course can be asked. This is problem because, when a new question is asked all the question in the discussion forum have to be compared with the new question for similarity. This can take up significant amount of computational time. To avoid this we have applied Random Forest classifier to find out the category of a newly asked question (with respect to Udemy it would mean finding which video the question relates to), if category is found only previously asked questions in the particular category are compared for similarity rather than the previously asked questions in the whole database. Second, Currently NPTEL is not course coordinator friendly. This is because lots of times there is redundancy in the questions asked, this is burden on the course coordinator because he/she has to answer to each and every version of the same question. This has been tackled by using techniques like GBDT, SVM to detect semantic similarity of questions. The Random Forest classifier achieved accuracy of nearly 73% and GBDT achieved accuracy of nearly 64%.
A Multiclass Object and Depth Estimation Deep Model for Humanoids Robots in a Soccer Simulated Dataset

With the evolution of humanoid robotics and its increasing use in diverse environments and tasks, robots have to interact with the environment and, therefore, understand it accurately to execute decision making. In this work, we present the process of collecting a new dataset for simulated soccer scenes over the V-REP simulator using the NAO robot as the preferred robot architecture. For this purpose, we simulated a RoboCup soccer challenge scenario and collected over 200k images that contain up to 4 classes of objects with their corresponding categories (goal, teammate, opponent, and ball), depth estimation and bounding boxes. An extended multiclass version of J-MOD2 was trained to validate the dataset. Results showed that even when working with a more significant number of classes than the original setup, we achieved better results than the original structure. We also tested the algorithm for partial occlusion and modifications in the scene, such as the opponents color and ball size, and we found that the system was able to generalize for these modifications.
Enriching Geospatial Representation for Ontology-based Aviation Information Exchange

The concept of System Wide Information Management (SWIM) for global Air Traffic Management (ATM) is to achieve various information exchange among related stakeholders, such as information about flights, routes, surveillance, airports, aircrafts, and weather. Although standard information exchange models are adopted in the SWIM concept, these models are different for each type of information. Therefore, ontology and data-linking technologies are required for the semantic integration of heterogeneous data. In this paper, we focus on the geospatial information contained in various data, and propose a method for extending the geospatial representation of the existing aviation ontology. This enables advanced geospatial searches using geometry information and facilitates integration of heterogeneous data.
Monocular Depth Estimation with Traditional Stereo Matching Information

Monocular depth estimation is to perform pixel-level depth estimation on single perspective image, and the methods based on deep learning have shown superior effect for the challenging task. However, the supervised depth estimation methods need costly ground truth labels, which require professional equipment. To solve this problem, we study the unsupervised monocular depth estimation algorithm combining traditional stereo knowledge. We use the unsupervised network based on the reconstruction method as the baseline, which can be trained without direct supervision from ground truth depths leveraging image synthesis on sequences or multi-views images, so that it can improve the accuracy of prediction results without applying any annotations such as LIDAR depth in KITTI. Experiments results on the publicly database KITTI (Eigen split) have shown the promising effectiveness of the proposed method, and compared with state of the art unsupervised algorithms, it has achieved competitive performance in objective evaluation indicators.
QUICKSAL: A small and sparse visual saliency model for efficient inference in resource constrained hardware

Visual saliency is an important problem in the field of cognitive science and computer vision with applications such as surveillance, adaptive compressing, detecting unknown objects and scene understanding. In this paper, we propose a small and sparse neural network model for performing salient object segmentation that is suitable for use in mobile and embedded applications. Our model is built using depthwise separable convolutions and bottleneck inverted residuals which have been proven to perform very memory efficient inference and can be easily implemented using standard functions available in all deep learning frameworks. The multiscale features extracted along the layers with deep residuals allow our network to learn high quality saliency maps. We present the quantitative results of our QUICKSAL model with multiple levels of model sparsity ranging from 0% to ~96%, with the non-zero parameter count varying from ~3.3M to ~0.14M respectively - on publicly available benchmark datasets - showing that our highly constrained approach is comparable to other state-of-the-art approaches (parameter count ~35M). We also present qualitative results on camouflage images and show that our model can successfully distinguish between the salient and non-salient parts even when both seem blended together.
A Study of Ciphertext Fuzzy Retrieval Based on Information Matrix

To the ciphertext retrieval and the security of ciphertext database, we propose a ciphertext fuzzy retrieval method based on the information matrix in this paper. On the basis of the Bloom Filter, we introduce the index of the information matrix and the digital disturbance in the process of index generation, after which we performe the string conversion encryption. It not only supports the fuzzy search function of strings, but resists similarity attacks as well. With the DFR method during ciphertext retrieval, the first stage of filtering is completed by index matching, and then the exact retrieval is performed. Experiments and safety analysis show that the scheme has more advantages in efficiency and safety.
Towards a Disciplined Methodology to Generate the E-Business Information System Models from the Business Value Model

Nowadays, the software industry has known significant growth, while the end-users have become very demanding. In this sense, many approaches have appeared among them we have the Model Driven Architecture (MDA) approach, which is initiative of the Object Management Group (OMG), considering the model as the central entity in the software systems development process. Our contribution in this paper is to suggest the idea of a new disciplined methodology based on the MDA approach to generate the whole of E-business Information System (IS) models from the business value model automatically. The purpose is to facilitate and automate the development process of any E-business IS, passing from a level of abstraction to another of the software development life cycle, via the transformation models key.
DSM in Forecasting and Scheduling for Improving Integration of Renewable Energy Generation to the Grid

The integration of renewable energy into the grid has become a challenge and faces two fundamental technological problems, namely variability and location. As renewable resources are focused far from the customers, it requires extra-long length, high-capacity transmission to coordinate the supply with the demand. It is essential to recognize the fluctuation and the vulnerability when examining and anticipating the tasks of the power grid. The test of the changeability can be met by exchanging conventional generation goal in or out in response to the possible estimates of weather by forecast and schedule deviation settlement mechanism (DSM). The deviation settlement mechanism is a penalty to the generator who under injects or over injects power into or outside the state with a buffer limit to bring awareness to the generator so that the forecast generation almost meets the actual generation taking the accuracy & AvC into account, in this paper the DSM calculations for inter and intra state has been shown and the comparison between less and more deviation pattern is shown for both solar and wind substations.
Identifying Security Challenges in the IoT for the Public Sector: A Systematic Review

This chapter reviews the expanding role of the Internet of Things (IoT) in our lives as well as the security concerns of IoT. While IoT has expanded enormously in recent years both in the private and public sectors where it has enhanced the quality of life, it has also created potential security risks for users in various ways, such as in enabling unauthorized access and misuse of personal information, facilitating attacks on other systems, and creating safety risks. Even though these risks have been already common in cyberspace contexts, the introduction of IoT has increased these risks given its role in expanding the Internet and its connections to every aspect of our daily lives. This chapter will provide a systematic review of the current literature of IoT in order to identify IoT security challenges, and to offer recommendations for responding to these challenges. As a result of our study, we identified pervasiveness, privacy, and vulnerability as main challenges that are discussed in the literature. In this research, we also compiled some recommendations such as encryption, cryptology, authentication, authorization, and advanced security frameworks, schemes, and protocols to respond current security challenges in the IoT. Policy recommendations are also discussed to give ideas to policymakers about IoT security.
Multi-keyword Ranked Searchable Encryption Supporting CP-ABE Test

Internet of Things (IoT) and cloud computing are promising technologies that change the way people communicate and live. As the data collected through IoT devices often involve users' private information and the cloud is not completely trusted, users' private data are usually encrypted before being uploaded to cloud for security purposes. Searchable encryption, allowing users to search over the encrypted data, extends data flexibility on the premise of security. In this paper, to achieve the accurate and efficient ciphertext searching, we present an efficient multi-keyword ranked searchable encryption scheme supporting ciphertext-policy attribute-based encryption (CP-ABE) test (MRSET). For efficiency, numeric hierarchy supporting ranked search is introduced to reduce the dimensions of vectors and matrices. For practicality, CP-ABE is improved to support access right test, so that only documents that the user can decrypt are returned. The security analysis shows that our proposed scheme is secure, and the experimental result demonstrates that our scheme is efficient.
ASIC Implementation of Rabbit Stream Cipher Encryption for Data

Over a network to secure information and to remain confidential cryptography plays a very important role in today's life. Cryptography is an method of converting information to a non-understandable form and then being able to convert it to the understandable form by the recipient. High level of security can be used for computing small power using lightweight stream ciphers. The new pipelined approach is proposed for Stream encryption means each letter one by one followed by the changing the encryption key after each letter. Here the work presents the design, simulation, and synthesis. Further the design is developed using cadence EDA physical design tools for optimizations on constraints like area, delay and low power consumption. Validation of data transmission in secure form (i.e., audio, image and text) using rabbit stream cipher algorithm is done. The rabbit algorithm is implemented using Verilog HDL simulated using Modelsim 10.4a synthesis using Xilinx ISE Design Suit 14.2 and also CADENCE.
Measuring and Comparing Immersion in Digital Media Multitasking

In terms of digital media usage, immersion refers to user involvement in and focus on a single activity. However, the commonality of multi-tasking raises questions regarding whether one could enjoy immersion when using more than one media at the same time. Self-report questionnaires and eye trackers were used to measure the immersive experiences while playing video games and watching a television program at the same time. While we found evidence of immersion across the two activities while multitasking, some immersion dimensions were significantly weaker. However, we also noted that immersion experiences from multiple media might be cumulative. A possible explanation for our results is that the act of switching between two media compensated for any down time, users could abandon a less attractive medium and switch to the other, resulting in an impression of continuous immersion in the overall multitasking experience. On the other hand, keeping active awareness of other media beyond the current focus might be a primary cause of immersion degradation.
Performance Analysis of Parallel Implementation of PCA-based Face Recognition using OpenCL

Face is one of the most commonly used biometrics for identifying a person uniquely. Face Recognition is a technique of identifying an individual and verifying their identity using the face. It finds applications in many areas including forensic investigations, security systems, mobile applications etc. All these systems require fast face recognition rate. But the complex structure of the face makes it difficult to speed up the process since huge computations are involved. One of the well known algorithms for face recognition is the Principal Component Analysis (PCA) Approach. This technique is very effective except for its higher computational cost. In this paper, PCA based face recognition is parallelized using OpenCL to speed up the computation and a comparison is made in terms of computational time required for serial and parallel implementation. Parallel implementation offers a 32-fold speedup over serial implementation proving the efficiency of the proposed method.
Approximate Depth Shape Reconstruction for RGB-D Images Captured from HMDs for Mixed Reality Applications

Depth sensors are important in several fields to recognize real space. However, there are cases where most depth values in a depth image captured by a sensor are constrained because the depths of distal objects are not always captured. This often occurs when a low-cost depth sensor or structured-light depth sensor is used. This also occurs frequently in applications where depth sensors are used to replicate human vision, e.g., when using the sensors in head-mounted displays (HMDs). One ideal inpainting (repair or restoration) approach for depth images with large missing areas, such as partial foreground depths, is to inpaint only the foreground; however, conventional inpainting studies have attempted to inpaint entire images. Thus, under the assumption of an HMD-mounted depth sensor, we propose a method to inpaint partially and reconstruct an RGB-D depth image to preserve foreground shapes. The proposed method is comprised of a smoothing process for noise reduction, filling defects in the foreground area, and refining the filled depths. Experimental results demonstrate that the inpainted results produced using the proposed method preserve object shapes in the foreground area with accurate results of the inpainted area with respect to the real depth with the peak signal-to-noise ratio metric.
Teaching Computer Graphics Programming Using a Game Development Engine

This poster presents our experience in using a Game Development Engine (GDE, i.e. Unity) for teaching introductory Computer Graphics (CG) courses. About a decade ago, standard textbooks used in CG courses began updating to adopt shader-based APIs and programmable GPUs. More recently, Graphics APIs evolved towards a lower level of hardware abstraction, reduced overhead, and multithreading capabilities. Once again, CG instructors have to either use outdated models, or change the way they teach. Acknowledging the divide between the development speed of GDEs, and the complexity and vastness of new graphics APIs, we show how the use of a GDE can help students understand core CG concepts, and still include shader-based programming, while low-level tasks are managed by the engine itself.
Generative Adversarial Network based Image Blur Compensation for Projection-Based Mixed Reality

Projection-based mixed reality superimposes an image on a real object by a projector. There is a problem that spatially nonuniform blurring occurs in the projected result due to defocus blur and subsurface scattering. As the solution, some methods of applying blur compensation to an input image before projecting have been studied. In this paper, we propose to use a generative adversarial network (GAN) that computes the compensation image from an input image and a projected result.
Role of AI in Gaming and Simulation

The overall purpose pursued in this paper is to learn more about gaming and simulation in general and how artificial intelligence is being implemented into them and all the possibilities for future in this scenery. The mechanism of simulation and gaming go hand in hand where artificial intelligence has been coming in clutch to be related these two in all the aspects we can think of. All the possibilities for the AI in simulation have been discussed along with gaming while learning about the problems we face when trying to pester AI in applications as it obviously may have some drawbacks too. All the different roads that can be taken with these combinations are something the paper has focused on. The trend of virtual reality has taken off recently and thus it is only logical that AI may be implemented into that too therefore we discuss that here. AI helps in optimization of programs and also saves labor as the AI can be taught to learn and work alone. AI can imitate human ways while avoiding the drawbacks a human may have. Mapping environments, making levels and characters are just about some examples that are being helped by AI in gaming while in simulation the AI helps in human interaction and creating scenarios while trying to ignore the danger that an actual experiment might have against a simulation. Social realism in gaming and simulation is just another important thing that is discussed as it is something really important to have that if an AI can learn then we might go a long way.
Utilizing Dynamic Mean Quantum Time Round Robin to Optimize the Shortest Job First Scheduling Algorithm

Scheduling refers to a set of policies and mechanisms to control the order of work to be performed by a Computer system. Utilizing Dynamic Mean Quantum Time Round Robin to Optimize the Shortest Job First Scheduling Algorithm is a CPU scheduling algorithm that uses the dynamic mean of the burst time to be used as the quantum time for Round Robin. The idea for this approach is to make the CPU Scheduler to arrange the processes in ascending order in the burst time and go to the least time that will be processed then it will calculate the dynamic mean from the burst time to be used for the quantum time for the Round Robin. Shortest job first selects the waiting process with the smallest execution time to execute next with the disadvantage of having starvation for being a greedy-algorithm which therefore implemented with Round Robin that will help the Operating System know the burst time and sort them to eliminate the starvation. There are some scheduling criteria, on the basis of these criteria we analysis and determine which scheduling algorithm is best. The performance of the optimized Shortest Job First Scheduling Algorithm was compared with Round Robin, Shortest Job First, and First Come First in reducing average waiting time, average Turnaround Time and average Response Time in some cases. Compared results from the three test cases showed that our algorithm performs better than Round Robin, and First Come First Serve in some cases but falls behind Shortest Job First where in the optimization of Shortest Job First Dynamic Round Robin (oSJFDRR) scheduling algorithm reduces the average Turnaround Time and average Waiting Time.