Reviewerly/exHarmony huggingface dataset:
An Efficient E-Commerce Design by Implementing a Novel Data Mapper for Polyglot Persistence

E-commerce applications are under tremendous pressure of performance with the increase of data, users and cutthroat competition. They need to optimize their designs to handle scalability and availability and at the same time ensure consistency. Traditional data layers were built with single type of data store and focused on very few database qualities. Traditional RDBMS was very effective if only consistency was desired. NoSQL stores are very effective if either availability or scalability was desired. But in an e-commerce application, we need all the properties all together. Hence, there is a need of hybrid data layer or polyglot persistence in the data layer. We need a mix of SQL and NoSQL both in the data layer of e-commerce. Implementing polyglot persistence needs to take care of appropriate insertion and retrieval. Data should be inserted in their respective database, and retrieval should also be done accurately. Polyglot persistence requires a data mapper to do this job. Here, we have designed a novel data mapper with adapts to the situation and executes the commands.
Modified differential evolution algorithm for solving minimum spanning tree

Differential evolution (DE) is a stochastic approach based on the concept of natural evolution. It is very simple and robust evolutionary algorithm. DE has three simple steps and it is easy to implement. It is a popular meta-heuristics that efficiently solved various complex optimization problems. This paper addressed the minimum spanning tree problem. Here a new variant of DE with new scaling factor inspired by peregrine preying pattern. In addition to new scaling factor it make use of global best solution for updating current solution. Results shows that modified DE performs significantly better than other competitive algorithms for considered minimum spanning tree.
TLS Cipher Suite: Secure Communication of 6LoWPAN Devices

Internet of Things (IoT) is a large-scale distributed network which connects smart products and things by the internet. Consequent to the exponential growth of this field is a rise in security and privacy challenges rooted in the vulnerability of smart hosts. This paper focuses on addressing a key security goal of the 6LoWPAN protocol for IoT devices - the need for a secure, yet lightweight encryption mechanism at the transport layer. Due to the highly resource-constrained nature of devices which operate with 6LoWPAN protocol, received messages are not individually checked at the application layer. End-to-end encryption measures cannot be implemented as intermediate routers in the sensor-networks used in IoT require access to entire message packets, as opposed to just the headers as in conventional networks. The 6LoWPAN protocol therefore relies on security measures which operate at the transport layer such as TLS. This paper aims to present a new, low-energy TLS cipher suite which serves a dual role as a secure hash function, thereby contributing to the stronger prevention of application layer threats such as message fabrication, interception and modification.
Technology of Composite Code Forming in The Spatial-Spectral Description Significant Microsegments

A method of composite coding of a significant microsegment of video frame at the codogram level of its syntax representation in the spectral space has been developed by constructing sequences of code structures of uniform length. The method of composite coding based on filling out redundant codogram sequences has been further developed. The distinctive features of the method are that single code designs are built for the codograms of significant microsegments of the video frame in spectral space using local-positional systems, where: positional-basic component is codogram of significant component of transformation, positional-complementary codogram - codogram lengths of chains zero component of the transformation. This method can be used in addition to JPEG, the modification itself will be an insert after DCT and quantization.
Interest Estimation for Images Using Eye Gaze-based Visual and Text Features via DLPCCA

This paper presents a new method to realize estimation performance improvement of user-specific interests for images. The proposed method computes projections which transform visual and text features to eye gaze-based features that reflect user's interests by utilizing discriminative locality preserving canonical correlation analysis (DLPCCA). DLPCCA can calculate projections suitable for interest estimation by considering the class information and locality of data structure. Experimental results are shown for verifying the effectiveness of our method.
Fabrication and demonstration of ultra-compact 3D measurement module using silica-based planar lightwave circuit

We fabricated an ultra-compact optical module using a silica-based planar lightwave circuit (PLC) for performing three-dimensional (3D) measurements. The module with a fiber, camera, lens, and flexible printed circuits is small enough to be attached to a tube with an inner diameter of 2.5 mm. The PLC is used as a fringe projector with a phase-modulation function and substrate for mounting the camera. A 3D measurement system based on a phase-shifting method was constructed using the module, and 3D profile measurements were performed on objects with thicknesses of 1.0, 2.0, and 3.0 mm. The experimental results indicated that the reconstructed shapes of the objects were clear enough for them to be recognized and the measurement variation was about 0.1 mm.
Applying Prefixed-Itemset and Compression Matrix to Optimize the MapReduce-based Apriori Algorithm on Hadoop

Apriori algorithm is the classical algorithm for mining association rules. However, it also has some problems, such as comparing the identical itemset repeatedly and scanning the external storage database frequently. Based on the previous research, this paper proposed a method of applying the prefixed-itemset and the compression matrix to optimize the connection step, pruning step, support counting step and transaction storage mode of the Apriori algorithm. The experimental results show that compared with the conventional Apriori algorithm, the optimized Apriori algorithm has more powerful mining efficiency and more excellent performance.
Real Time Object Detection in Surveillance Cameras with Distance Estimation using Parallel Implementation

Object detection is not only shaping how Computers see and analyze things but it is also helping in the behavior of how an object reacts to the change in its environment. The main application of these object detection sensors or software is to find the location of an object in space or to track its movement. Object detection has infinitely many use cases and in this paper, we are introducing an application that will allow safety of users struck in a disaster and who need to be evacuated. In such cases the main thing to focus and to eradicate is camera noise, saturation and image compression. Our solution is to establish a connection between the person struck in a disaster with fire safety people. This works over a convolutional network that allows us to detect vulnerable things present inside a room that needs to be rescued and can also give an insight of any explosive inside the room. Our model uses Faster-RCNN and COCO which is a pretrained dataset. This allows real time object detection and classification on our network. Using this we were able to detect an object or a person and get him to rescue by providing them a shortest way out of that place. With this we were able to get an accuracy of more than 75% in our object detection model.
Visualizing the internal structure of polyethylene glycol-impregnated wood by digital breast tomosynthesis

This study proposes digital breast tomosynthesis (DBT) as a low-tube-voltage method for imaging wood artifacts treated with polyethylene glycol (PEG). In case of general clinical X-ray Computed Tomography (X-CT), PEG-impregnated wood images typically suffer from low contrast between the PEG and the tree-ring. Because X-CT uses high-tube-voltage X-rays that have high energy, they are transmitted regardless of the X-ray absorption difference of the substance, and therefore, it is not suitable for imaging PEG-impregnated wood. Mammography uses low-tube-voltage X-rays, and therefore, it is suitable for delineating substances with small X-ray absorption differences. However, although mammography can produce high-contrast images of wood, it cannot distinguish three-dimensional (3D) structures such as tree rings, because those are projection images. DBT is a type of mammography used to enhance contrast using low-tube voltage, and it enables imaging 3D structures by exposure X-rays to objects several times changing the exposure angle, and it can obtain quasi-computed tomography. Therefore, we believe that by applying DBT to dendroarchaeology, it would be possible to obtain high-contrast, high-resolution images in the visualization of the internal structure of wood. In this study, we used clinical X-CT, mammography, and DBT to obtain images of wood after PEG impregnation, and we evaluated the internal structure of the wood and the visibility of annual rings. We obtained DBT images as a tomogram with a thickness of 1 mm, which eliminated the distortion of tree rings in the sagittal direction and duplication of the PEG and the tree-ring. Further, tree-rings were easily visualized without a noticeable blur, and the DBT contrast was improved compared to clinical X-CT contrast because DBT was performed at low voltage. Important wooden artifacts excavated from ruins were preserved by PEG. Therefore, this method can be expected to become a very useful tool for dendroarchaeology when used as a complementary tool for microfocus X-CT.
Quotients by Idempotent Functions in Cedille

We present a simple characterization of definable quotient types as being induced by idempotent functions, and an encoding of this in Cedille (a dependently typed programming language) in which both equational constraints and the packaging that associates these with elements of the carrier type are irrelevant, facilitating equational reasoning in proofs. We provide several concrete examples of definable quotients using this encoding and give combinators for function lifting (with one variant having zero run-time cost).
Optimization of Bug Reporting System (BRS): Artificial Intelligence Based Method to Handle Duplicate Bug Report

Bug tracking and reporting are some of the most critical activities/steps in software engineering and implementation which has a direct impact on the quality of tested software and productivity of resources allocated to that software. Bug Reporting System (BRS) plays an important role in tracking all essential bug reports during software development life cycle (SDLC). Duplicate Bug Reports (DBR) have an adverse effect on the software quality assurance process as it enhances the processing time of bug triager whose job is to keep track of all bug reports and also on application developers, to whom bug tickets are assigned by the triager. Duplicate bug reports if remain unidentified may result in enhancing bug handling time (rework) and decreasing overall team performance. However identification of duplicate bug report remains as a critical task as it is a tough job to manually identify all second images of earlier reported bug. In this paper we have proposed an enhancement in existing BRS which uses artificial intelligence based intelligent techniques to detect the existence of a duplicate bug. Every new bug reported to the system will be marked with an identification tag. A bug containing duplicate tag will be phased out from the bug repository which will not only reduce the additional effort on bug triage but also improve the system’s performance.
An Experimental Analysis on the Necessary and Sufficient Conditions for the RRT* Applied to Dynamical Systems

In this paper, we study some properties of several local planners for nonholonomic dynamical systems to achieve asymptotic global optimality through the RRT*. More specifically, we study the conditions that a local steering method must have to produce global optimal trajectories in an environment with obstacles. The main properties we analyse in the steering methods are the following: (1) Whether or not the steering method produces local optimal motion primitives (optimal letters). (2) Whether or not the steering method concatenates the local optimal primitives in such a way that the resulting concatenation is also optimal (optimal words). (3) Whether or not the steering method produces trajectories that respect the topological property. Experimentally, it is studied how those properties affect the speed of convergence to the globally optimal solution, moreover, their sufficiency and necessity is also validated, all making use of the problem of finding the time-optimal trajectories for a differential drive robot in the presence of obstacles. We also discard conditions that show not to be necessary and we give some insight on the necessary and sufficient conditions for the RRT* to asymptotically converge to optimal trajectories, which is indeed the sough research target.
African Buffalo Optimization Algorithm for Personalized Diet Optimization

Food provides our body energy, vitamins, and other essential nutrients needed by the body to sustain us for our day-to-day activities and to function properly. Malnutrition is a state of under-nutrition or over-nutrition that can lead to one or more Diet Related Diseases (DRDs). Therefore, a personalized diet optimization and recommendation is a vital area to be investigated when attempting to prevent DRDs and meet nutritional necessities is the objective of this study. African Buffalo Optimization (ABO) algorithm was compared with Particle Swarm Optimization (PSO) based on ease of convergence. ABO was applied to diet optimization because it produces very competitive result and it is also one of the recently developed metaheuristic. Its result was compared with Particle Swarm Optimization (PSO) because PSO is one of the commonly used metaheuristic. ABO was seen to achieve convergence in lesser time.
Fast ReRoute Model with MultiMedia Quality Protection

In this work, a tensor fast rerouting flow-based model with the protection of the multimedia quality was proposed. In the presented model, the following constraints and conditions were introduced: multipath routing strategy implementation, flow conservation conditions under consideration of possible packet loss at the network nodes, as well as links overload prevention that used both in the primary or the backup paths. At the same time, the conditions of Quality of Experience level protection defined in terms of multimedia quality over the primary and the backup paths, which is the novelty of the presented solution. The tensor network formalization allowed to obtain such conditions and calculate the Quality of Service indicators, namely, packet loss probability and average end-to-end delay for audio and video flows transmitted in the multimedia session by the primary and backup paths. The condition under maximization of the overall network performance has been selected as the optimality criterion.
Performance Analysis of New SCADA Interface Developed in C# Environment

The industrial process is monitored and controlled by the Supervisory Control and Data Acquisition System that are installed throughout the plant. Currently, the commercially available SCADA systems are not in the approach of the small industries due to the higher installation cost and are not benefited enough by this technology. This research investigates the performance of the SCADA software developed in C# environment and the comparison with commercial SCADA software in the aspects of cost effectiveness, performance and easiness in customization is presented.
Random Patterns Clothing Image Retrieval using Convolutional Neural Network

In this paper we present supervised semantic preserving deep hashing (SSDH) based random patterns clothing image retrieval framework. The dense existence of random patterns in Pakistani women's clothing (PWC) images makes them different and challenging as compared to the western women's clothing images. Owing to the diversity of PWC, we propose an efficient representation of their random patterns in terms of the basic patterns found in western women's clothing. We present a retrieval framework for PWC consisting of two stages which are attribute selection and binary coding of features. Regarding the attribute selection, we use the texture that better describes the dense patterns of PW C, and the binary coding of features is performed by using SSD H technique. In order to evaluate the performance of the proposed PW C retrieval framework we present a dataset of PWC images. Experimental evaluation shows that the proposed representation is capable of dealing with the high variations of PWC random patterns effectively.
Edge image detection combines Laplace operation with convolution technique to produce drawing materials for children

Abstract The image object is a very interesting object. It is interesting as it can be processed even manipulated. The image cannot be separated from the computer. Computer can process images so that the value increases. There are many computer applications that can be used to process images. The development of the image is so rapid but does not touch the world of children yet. Although there are a lot of books that can be used as references for children, but there are not very interesting ones. By using computer technology, the image will have higher quality and beneficial for children. Of course there must be innovation to change the dynamics. The concept of edge detection combined with computerized based methods can be a good solution. Edge detection can be used to recognize patterns from an image. It can identify the edge in the image. In this study an edge detection system will be designed and implemented by using Laplace operators and convolution techniques. The initial image will be constructed so that a pattern in the form of dots will be formed.
Modulation Classification Based on Kullback-Leibler Divergence

This paper proposes a modulation classification method based on Kullback-Leibler divergence (KLD). The proposed method involves computation of the empirical probability mass functions (PMFs) of the decision statistics and their subsequent comparison with the theoretical PMFs of the decision statistics under each candidate modulation scheme by using KLD. We use quadrature components of the received signal as the decision statistics to compute the PMFs and consider the classification of linear digital modulation schemes such as the phase shift keying and quadrature amplitude modulation schemes in an additive white Gaussian noise channel. Through computer simulations, we show that the proposed KLD-based method outperforms conventional Kolmogorov-Smirnov test-based methods in classification performance.
Scalable Parallel Genetic Algorithm For Solving Large Integer Linear Programming Models Derived From Behavioral Synthesis

Solving Integer Linear Programming (ILP) models generally lies in the category of NP-hard problems. Therefore, as the size of ILP models grows, the efficiency of exact algorithms for solving the models reduced significantly and for large models it is not possible to have the result. Genetic Algorithm (GA) is a metaheuristic method capable of adjusting and redesigning parameters and operations according to the characteristics of ILP models. Still GA has huge search space for large models and parallelization is a suitable technique to tackle this problem. This paper presents a scalable parallel GA to solve large ILP models derived from behavioral synthesis of digital circuits. We show that although models have non-binary variables, only binary variables are sufficient for coding chromosomes. We also use ”unknown” values for some genes to decrease the likelihood of inconsistency in the encoded constraints. Our experiments verify the efficiency and scalability of the proposed algorithm on multicore platforms. The proposed method outperforms IBM ILOG CPLEX 12.6 and MI-LXPM algorithm where the ILP models include 550 to 2258 int / binary decision variables. Also, the results indicate that the saturation point of using parallel processing elements for solving the large ILP models is at least 60.
Single-domain free logic and the problem of compositionality

Abstract In this paper, I will defend a new compositional semantics for single-domain free logic. This semantics makes use of a distinction between the semantic value of a singular term and its semantic referent . The semantic value of a singular term is conceived of as a set that either contains the semantic referent or no element at all. The semantic referent is the object that the term designates. Before I will introduce this new semantics for single-domain predicate and an S5-type modal logic in detail, I will present five related problems of compositionality of the standard semantics for these logical systems. After that I will show in detail how my new proposed alternative semantics can be used to solve nearly all of the outlined five problems.
Face Recognition Techniques using Statistical and Artificial Neural Network: A Comparative Study

Face recognition is the process of identifying a person by their facial characteristics from a digital image or a video frame. Face recognition has extensive applications and there will be a massive development in future technologies. The main contribution of this research is to perform a comparative study between different statistical-based face recognition techniques, namely: Eigen-faces, Fisher-faces, and Local Binary Patterns Histograms (LBPH) to measure their effectiveness and efficiency using real-database images. These recognizers still used on top of commercial face recognition products. Additionally, this research is comprehensively comparing 17 face-recognition techniques adopted in research and industry that use artificial-neural network, criticize and categories them into an understandable category. Also, this research provides some directions and suggestions to overcome the direct and indirect issues for face recognition. It has found that there is no existing recognition method that the community of face recognition has agreed on and solves all the issues that face the recognition, such as different pose variation, illumination, blurry and low-resolution images. This study is important to the recognition communities, software companies, and government security officials. It has a direct impact on drawing clear path for new face recognition propositions. This study is one of the studies with respect to the size of its reviewed approaches and techniques.
Usability of a Foreign Body Object Scenario in VR for Nursing Education

With this study, the researchers created a Virtual Environment (VE) where nursing students could practice managing a scenario of an adult patient with a respiratory foreign body object. The VE was exposed to rigorous usability and user experience testing with the help of two expert review panels, one made up of Computer Science experts and one of Health Science experts. Each panel evaluated the VE and scenario using heuristic evaluation and cognitive walkthroughs. The results and recommendations were implemented to improve the VE, thus enabling students to experience an accurate virtual scenario in their training. The improved VE based on the results of the expert reviews are presented in this paper.