Reviewerly/exHarmony huggingface dataset:
Scaled Nested Key Equation Solver for Generalized Integrated Interleaved Decoder

Generalized integrated interleaved (GII) codes nest short Reed-Solomon or BCH sub-codewords to form codewords of higher error-correction capability. They enable hyper-speed decoding and efficient failure recovery in distributed storage. Although the nested decoding can continue from the key equation solver (KES) result of the sub-codeword decoding, its discrepancy coefficient computation leads to long critical path. A recent nested KES architecture reduced the critical path to the same as that of the KES for sub-codeword decoding. Nevertheless, the nested KES has large area. This brief proposes a novel scaled nested KES algorithm and architecture to reduce the area requirement. Scaled versions of the polynomials are utilized to enable the sharing of product terms and the nested KES algorithm is reformulated to efficiently incorporate the scaling without changing the decoding output. As a result, the number of expensive finite field multipliers is significantly reduced. For an example code over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GF(2^{8})$ </tex-math></inline-formula> that corrects up to 28 errors, from architectural analysis, the proposed architecture achieves 17% area reduction with only one more gate in the critical path.
A First-order Logic for Reasoning about Knowledge and Probability

We present a first-order probabilistic epistemic logic, which allows combining operators of knowledge and probability within a group of possibly infinitely many agents. We define its syntax and semantics and prove the strong completeness property of the corresponding axiomatic system. 1
Performance Comparison of Convolutional Neural Network Models on GPU

Deep learning methods are used in many popular areas such: image processing, computer vision, autonomous vehicles, character recognition, audio and video processing. These methods require high processing power, such as graphics cards (GPUs), to obtain successful results in the solution of NP hard problems which have big data. In this study, performance comparison of convolutional neural network (CNN) architectures were performed on GPU. ResNet, VGGNet19 and DenseNet CNN models, and GPDS signature dataset were used for comparison. According to the obtained results, ResNet50 took up the least amount of GPU memory space. The best classification results were obtained with DenseNet121 and the second one was from ResNet50.
Review on e-Learning Environment Development and context aware recommendation systems using Deep Learning

Internet has open platforms for various domains to interact with each other. e-leaning is a domain which consistently getting attention because of “Learn any-time, anywhere” approach. Since, the start of e-learning environment development phase semantic web base ontology is used for describing and making relation between Learning Objects (LO's). Since contents are increasing every day, on this point ontologies are lagging to recommend. Development of recommendation using deep learning techniques are producing comparatively better results. Deep learning is now been used for classification, predictions, recommendations. It is also used in detection and segmentation techniques as well. This paper presents discussion various categories of recommendation systems. Then a comparative study is on deep learning-based recommendation systems. Major challenge that an e-learning environment is facing is because of “Cold-Start” and “Sparsity” in content based (CB) & collaborative Filter (CF) based recommendation systems. How to reduce Cold-Start and Sparsity is tried to find out in this paper.
An evolutionary Pentagon Support Vector finder method

In dealing with big data, we need effective algorithms; effectiveness that depends, among others, on the ability to remove outliers from the data set, especially when dealing with classification problems. To this aim, support vector finder algorithms have been created to save just the most important data in the data pool. Nevertheless, existing classification algorithms, such as Fuzzy C-Means (FCM), suffer from the drawback of setting the initial cluster centers imprecisely. In this paper, we avoid existing shortcomings and aim to find and remove unnecessary data in order to speed up the final classification task without losing vital samples and without harming final accuracy; in this sense, we present a unique approach for finding support vectors, named evolutionary Pentagon Support Vector (PSV) finder method. The originality of the current research lies in using geometrical computations and evolutionary algorithms to make a more effective system, which has the advantage of higher accuracy on some data sets. The proposed method is subsequently tested with seven benchmark data sets and the results are compared to those obtained from performing classification on the original data (classification before and after PSV) under the same conditions. The testing returned promising results.
Supportive Information to Find Victims from Aerial Video in Search and Rescue Operation

An unmanned aerial vehicle (UAV) is expected to be one of the powerful IoT tools in a search and rescue (SAR) operation. Notably, an aerial video would be most promising. However, in a SAR operation, a searching task via an aerial video compels laborious tasks such as high concentration to practitioners. In this paper, to reduce such a laborious task, we propose a method to provide supportive information, i.e., the number of persons, the time and thumbnails, for finding victims via an aerial video with an object detection algorithm. Also, to use it in a SAR operation, we need to meet the requirements for a dataset and lightweight process. Through the evaluation, we showed that we could get supportive information reasonably and displayed them on a GUI.
Proximity-Based Unification: An Efficient Implementation Method

Unification is a central concept in logic systems based on the resolution principle. As well, in knowledge representation, proximity relations (i.e., reflexive, symmetric, fuzzy binary relations) are useful for introducing semantics into a syntactic level by modeling the semantic closeness of different syntactic objects and managing vague or imprecise information. Proximity relations, in combination with the unification algorithm, make possible expressing certain forms of approximate reasoning in a logic programming framework. In this article, we use proximity relations in the context of a (fuzzy) logic programming system, called Bousi ~ Prolog, as a way of solving the limitations introduced by similarity relations (i.e., transitive proximity relations) to correctly represent fuzzy information. Recently, we introduced an accurate definition of proximity between expressions (terms or atomic formulas) and a new unification algorithm able to manage proximity relations properly. However, the so-called weak unification algorithm, which is an extension of Martelli and Montanari's unification algorithm supported by the new notion of proximity, does not have an efficient implementation. In this article, we present a method that facilitates such an efficient implementation, including an adaptation of the weak SLD resolution rule based on the new unification algorithm, and its integration and implementation into the fuzzy logic programming system Bousi ~ Prolog. A performance analysis to show its efficiency is also presented.
Optimal Fuzzy Clustering by Improved Biogeography-based Optimization for Leukocytes Segmentation

Automated leukocytes segmentation is a difficult process in case of histopathological images due to the presence of various morphological structures in the images. This paper proposes a new fuzzy clustering method using improved biogeography-based optimization method which segments the leukocytes in the histopathological images of inflamed rat skins. The proposed method uses the ratio of compactness and separation as an objective function for getting the better cluster quality. The performance analysis of the proposed clustering method is done on histopathological inflamed rat skin images using F1 score and aggregated jaccard index value. The experimental results depict that the proposed method outperforms the other existing clustering based segmentation methods.
Distributed Complementary Binary Quantization for Joint Hash Table Learning

Building multiple hash tables serves as a very successful technique for gigantic data indexing, which can simultaneously guarantee both the search accuracy and efficiency. However, most of existing multitable indexing solutions, without informative hash codes and strong table complementarity, largely suffer from the table redundancy. To address the problem, we propose a complementary binary quantization (CBQ) method for jointly learning multiple tables and the corresponding informative hash functions in a centralized way. Based on CBQ, we further design a distributed learning algorithm (D-CBQ) to accelerate the training over the large-scale distributed data set. The proposed (D-)CBQ exploits the power of prototype-based incomplete binary coding to well align the data distributions in the original space and the Hamming space and further utilizes the nature of multi-index search to jointly reduce the quantization loss. (D-)CBQ possesses several attractive properties, including the extensibility for generating long hash codes in the product space and the scalability with linear training time. Extensive experiments on two popular large-scale tasks, including the Euclidean and semantic nearest neighbor search, demonstrate that the proposed (D-)CBQ enjoys efficient computation, informative binary quantization, and strong table complementarity, which together help significantly outperform the state of the arts, with up to 57.76% performance gains relatively.
Elderly Health Monitoring System with Fall Detection Using Multi-Feature Based Person Tracking

The need for personalized surveillance systems for elderly health care has risen drastically. However, recent methods involving the usage of wearable devices for activity monitoring offer limited solutions. To address this issue, we have proposed a system that incorporates a vision-based deep learning solution for elderly surveillance. This system primarily consists of a novel multi-feature-based person tracker (MFPT), supported by an efficient vision-based person fall detector (VPFD). The MFPT encompasses a combination of appearance and motion similarity in order to perform effective target association for object tracking. The similarity computations are carried out through Siamese convolutional neural networks (CNNs) and long-short term memory (LSTM). The VPFD employs histogram-of-oriented-gradients (HoGs) for feature extraction, followed by the LSTM network for fall classification. The cloud-based storage and retrieval of objects is employed allowing the two models to work in a distributed manner. The proposed system meets the objectives of ITU Focus Group on AI for Health (FG-AI4H)under the category, “falls among the elderly”. The system also complies with ITU-T F.743.1 standard, and it has been evaluated over benchmarked object tracking and fall detection datasets. The evaluation results show that our system achieves the tracking precision of 94.67% and the accuracy of 98.01% in fall detection, making it practical for health care system use. The HoG feature-based LSTM model is a promising item to be standardized in ITU for fall detection in elderly healthcare management under the requirements and service description provided by ITU-T F.743.1.
Path planning of mobile robot based on Improved RRT Algorithm

In the path planning of mobile robot, aiming at the problem that the rapidly-exploring random tree (RRT) algorithm, which adopts the global uniform sampling strategy, leads to strong randomness and non-progressive optimization in the process of path growth, an improved RRT algorithm is proposed. Firstly, in the sampling stage, we establish the region sampling to select target points, and add gravitational component to solve the problem of RRT algorithm which is too random. Secondly, the obstacle expansion method is adopted to solve the limitation of long sampling time when the growing tree is close to the obstacle area. In view of the difficulty of the narrow-channel robot to pass, the ”Bridge Test” is introduced to guide the robot to walk. The improved algorithm effectively reduces the occupied space memory, running time and number of nodes, shortens the path length at the same time. By comparing the improved RRT algorithm with the basic RRT algorithm and the asymptotically optimal bi-directional rapidly-exploring random tree (B-RRT*) algorithm, the simulation results show that the improved RRT algorithm has shorter path and better time, and is more efficiently.
Dynamic Load Balancing Approach for Minimizing the Response Time Using An Enhanced Throttled Load Balancer in Cloud Computing

Cloud computing covers a broad range of cloud-based service models such as SaaS: Software as a Service, PaaS: Platform as a Service, IaaS: Infrastructure as a Service, XaaS: Everything as a Service, NaaS: Network as a Service and RaaS: Recovery as a Service. Whether the users are looking at SaaS, PaaS, IaaS, XaaS, NaaS or RaaS, the estimations are the same; fast development of the workloads placed on the VMs in the cloud and a maximized percentage of the IT budget moving toward cloud computing. Therefore, there is a need for an enhanced VM level load balancer to balance the VM level load. In this research, different dynamic load-balancing methods and enhanced algorithms are analyzed to develop a load balancer for allocating the workloads evenly to the VMs and to migrate the excess tasks from the overloaded VMs to the underloaded VMs. The motivation of our research is to propose an Enhanced Throttled Load Balancer for allocating the workloads evenly to all VMs and for minimizing the delay time and response time of the service. The proposed load balancer distributes the arriving requests evenly to all VMs and allocates the resources dynamically. Using the threshold value, the proposed load balancer categorizes the VMs as overloaded VMs, underloaded VMs and balanced VMs. If there is an overloaded VM then the proposed Enhanced Throttled Load Balancer finds out the suitable underloaded VM and automatically begins the task migration process to migrate the task to the underloaded VM from the overloaded VM thereby balance the load on VMs. The main use of the proposed load balancer is to balance the load on VMs and to control the overhead of the VM. This paper presents a dynamic load balancing approach using Enhanced Throttled Load Balancer that aids to reduce the delay time and the response time of the service.
A Study on Machine Learning Approaches for Named Entity Recognition

Natural Language Processing (NLP) is one of the sub-parts of Artificial Intelligence which normally focuses on empowering computers to understand and operate on human languages and to get computers closer towards understanding a language as a human does. Named Entity Recognition (NER) is core of NLP systems. NER is a process of automatic identification of named entities in a given text or document. Named entities are real world objects or in general named entities are proper nouns like name of person, location, date and time expression etc. The recognition and extraction of real named entity is very important for solving difficulties in many research areas like Question Answering and Summarization Systems, Information Extraction, Machine Learning, Semantic Web Search and Bio-informatics, Video Annotation and many more. In this paper the major focus is given on comprehending different types of NER and approaches applied for NER especially different machine learning models used for identification of Named Entities.
Dairy Cow Health Monitoring System Based on NB-IoT Communication

The cow breeding work has been using inefficient methods of artificial observation and physiological test to determine the estrus status of cows. Furthermore, these methods have disadvantages of high missed check possibilities for the estrus is usually long-cycled and has a very short duration. The cow estrus detection system is based on NB-IoT communication (Narrow-Band Internet of Things Communications). Some researches have shown that cows' exercise capacity and their physical status such as temperature will change significantly during their estrus. The system is used to discover the unusual changes of cows' physical status to determine the cows' estrus status and report to administrator in time. The system makes it possible to reduce workers' pressure and improve economic returns of pastures. The monitor worn on the neck records cow's physical status such as body temperature and exercise capacity and then upload to our server via NB-IoT communication modules. Furthermore, the data will be analyzed and processed by our application on android. Also, image recognition technology will be used to determine the cow's estrus behavior as a supportive method to improve the accuracy of our system.
Depth Sensing by Near-Infrared Light Absorption in Water

This paper introduces a novel depth recovery method based on light absorption in water. Water absorbs light at almost all wavelengths whose absorption coefficient is related to the wavelength. Based on the Beer-Lambert model, we introduce a bispectral depth recovery method that leverages the light absorption difference between two near-infrared wavelengths captured with a distant point source and orthographic cameras. Through extensive analysis, we show that accurate depth can be recovered irrespective of the surface texture and reflectance, and introduce algorithms to correct for nonidealities of a practical implementation including tilted light source and camera placement, nonideal bandpass filters and the perspective effect of the camera with a diverging point light source. We construct a coaxial bispectral depth imaging system using low-cost off-the-shelf hardware and demonstrate its use for recovering the shapes of complex and dynamic objects in water. We also present a trispectral variant to further improve robustness to extremely challenging surface reflectance. Experimental results validate the theory and practical implementation of this novel depth recovery paradigm, which we refer to as shape from water.
Research on Applications of Artificial Intelligence in Business Management of Power Grid Enterprises

Due to the fast development of deep learning, products and services based on artificial intelligence (AI) have been transformed from concept to reality. However, there still exist huge challenges when applying AI technologies in traditional industrial enterprises. This paper studies those key technologies that support business management applications from power grid enterprise perspective, where state-of-the-art use cases have been investigated. Finally, potential developing trends of business management are foreseen by considering the innovation of AI technologies.
Wheelchair Training Virtual Environment for People with Physical and Cognitive Disabilities

This paper introduces the virtual reality and Brain Computer Interface based environment for the powered wheelchair training of paralyzed persons. Virtual reality allows to create the environments in which users can navigate wheelchair without risk and flexibly. Brain Computer Interface devices allow to control things with EEG brain signals. In this study, we have developed the environments that are configured with virtual reality and EEG devices (Emotiv Epoc+). We have performed experiment on 16 subjects using different parameters and analyzed that virtual reality based environments can be significantly useful for the training of paralyzed persons. We observed significant improvement in wheelchair controlled by EEG signals using virtual reality based environments. Our results revealed that virtual reality based training of wheelchair controlled by EEG signal gives significantly positive feedback and results. Furthermore, improvement on Brain Computer Interface control is higher than manual control.
Deep Convolutional Neural Networks for Breast Cancer Detection

Breast cancer is one of the main causes of cancer death worldwide. In the midst of the treatment of different disorders and diseases, one critical aspect in saving a patient is early detection. Correct detection and assessment of mammograms is hindered by human-error and inter-observer variations between pathologists. Existing convolutional neural network structures have shown promise in detection, but are hindered in their requirements for very large datasets to train on. The purpose of this paper is to explore a streamlined method classification of hematoxylin and eosin (H&E) stained tissue cancer mammograms into non-carcinomas and carcinomas using a small training set. This is done by the creation of more sample sets through changing elements of the data such as shear ratio and rotation. We assumed a 4-layer DCNN (deep convolutional neural network). We first train the DCNN with our augmented dataset, increasing dataset size by x200. We implement a highly accurate and reduced chance of overfitting gradient boosting algorithm. The overall classification accuracy of benign versus malignant was 88%.
A Detection System for Stolen Vehicles Using Vehicle Attributes With Deep Learning

There is phenomenal growth in numbers of vehicles on road as well as in numbers of theft cases. In a few countries, vehicle theft cases are believed to be one in every 10 minutes. In such high-intensity scenario, it is not possible to identify the stolen vehicle with traditional methods such as manual check, RFID base technologies where it is easy to manipulate and remove. This paper presents a design with the implementation of a system based on deep learning technique where SSD algorithm is coupled with K-Nearest Neighbors algorithm and convolutional neural network classifier, to identify the stolen/suspicious vehicles without human intervention. This design operates in two stages: At first stage Tensor flow, Tensor Flow detection API from Google is used to detect the vehicle with sub-modules to identify color and registration number from a real-time video stream. In second stage it compares the extracted characteristic i.e. vehicle registration number and color with the RTO record. If in comparison it observes any anomaly in two sets or matched with stolen vehicle complaint record at police station. It would notify the concerned authorities. A prototype model is developed based on the presented design.
Optimization of Hadoop MapReduce Model in cloud Computing Environment

In recent years data analysis has become one of the trending topic among the researchers. Moreover, Information is the new baseline of all organization, as to grow the faster and bigger. Relevant information provides the flexibility to know the like and dislike of customer and to get the relevant information requires the analysis of huge information that is stored in various format. Hadoop constitutes of two basic model i.e. Hadoop Distributed File system (HDFS) and MapReduce, Hadoop is used for processing the huge amount of data whereas MapReduce is used for data processing. Hadoop MapReduce is one of the best platform for processing the huge data in efficient manner such as processing of web logs data. In this paper, we have proposed optimized HPMR (Hadoop MapReduce) model, which maximizes the memory utilization for the task and balances the performance between the I/O system and CPUs. HPMR contains the three phase i.e. Hadoop, Map and Reduce just like any other Hadoop model, however HPMR optimizes all three phase i.e. map, shuffle and reduce. Moreover, to optimize the memory model HPMR opts for dynamic terminology and input/output optimization is done through the dual operation. Moreover, in order to evaluate the performance of our model we have performed the Word-Count application on the Wikipedia data of size 128 Mb, 256 Mb, 512 Mb, 1 GB and 2 GB. The comparative analysis shows that our model optimizes nearly 30% better than the existing one.