Reviewerly/exHarmony huggingface dataset:
On Symmetry and Initialization for Neural Networks

This work provides an additional step in the theoretical understanding of neural networks. We consider neural networks with one hidden layer and show that when learning symmetric functions, one can choose initial conditions so that standard SGD training efficiently produces generalization guarantees. We empirically verify this and show that this does not hold when the initial conditions are chosen at random. The proof of convergence investigates the interaction between the two layers of the network. Our results highlight the importance of using symmetry in the design of neural networks.
The Generalized Bayes Method for High-Dimensional Data Recognition with Applications to Audio Signal Recognition

High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application.
Dense Aggregation Based Efficient Network for Image Semantic Segmentation in Edge Intelligent Tasks

The image semantic segmentation algorithm required by edge intelligence needs to have high accuracy and real-time performance at the same time. In this paper, a new image segmentation network: Dense Aggregation based efficient neural network (DA-ENet) is proposed. The proposed network aggregates feature maps of each two adjacent stages of DA-ENet into an aggregation feature map, and uses shortcut connections connect each encoder stage feature map to the two decoder stages. In DA-Enet the linear propagation way of original ENet was changed. DA-Enet uses aggregations to realize feature fusion at different stages of the network and reduce the number of network parameters. DA-Enet uses shortcut connections to enhance the propagation and reuse of features. Aggregations and shortcut connections make DA-ENet a densely connected network. In densely connected network, each layer receives additional supervision from the loss function. Therefore, DA-Enet will be easier to train and has higher accuracy.The proposed DA-ENet was evaluated on three standard datasets: CamVid, CityScapes and SUN RGB-D. The experimental results demonstrate that DA-ENet has similar inference speed but higher segmentation accuracy to ENet.
Execution Migration of Managed Languages Programs Among Heterogeneous-ISA Processing Units

Modern processors couple processing units of different instruction set architectures (ISAs). Programs built for one ISA cannot run on others, and migration between ISAs is not straightforward. Different methods have been proposed to support transparent migration among heterogeneous-ISA processing units. However, methods that address managed code, such as Java, shown to be much slower than the ones for unmanaged code (or native).
Finite Vector Spaces and Block Designs

Let q be a prime power, Fq the finite field with q elements, GLn(Fq) the general linear group of degree n over Fg, and Vn(Fq) the n-dimensional vector space over Fq. GLn(Fq) will be viewed as a transformation group of Vn(Fq). We will first prove a theorem concerning the transitivity of GLn(Fq) on a set of some subspaces of Vn(Fq), and then use it to construct a number of PBIB designs and a BIB design, the parameters of which are also computed.
Ship target detection and fine-class recognition based on course-to-fine cascade neural networks

We proposed a new method to detect the ship targets in large scale remote sensing images, which can recognize the fine-class ship targets more than just detect the targets. The pipeline consists of cascade neural networks to reach higher detection and recognition performance. To detect better, we use different threshold in the cascade module, so that we can eliminate background interference better. We also use multi-direction boxes to detect the ship targets to get better performance. To recognize better, we firstly recognize ship category in a module, then we recognize the type in another module to get fine-class ship recognition results. Through experiments on the Google Earth remote sensing image data set collected by ourselves and HRSC2016 data set, it shows that the method is effective in ship target detection and fine-class recognition of remote sensing image, and the performance is improved compared with the previous algorithms.
Reducing Search Space in Subgraph Matching Problem

Subgraph matching problem refers to finding query graphs in a large graph. The size of search space in subgraph matching depends on the size of large graph. Due to this large search space, some methods have been proposed to reduce the computational time of matching by preprocessing the large graph. The structural indexing methods restrict the potential occurrences of subgraphs. However, a large percent of these candidates are false positives, which waste resources in matching time. In this paper, we propose a method to find and remove false positive candidates using spectral features in localities. Experiments on biological datasets demonstrate the efficiency of our method in terms of pruning the search space and reducing the matching time.
Designing of integrated information system (IIS) scheme for private higher education in indonesia: a strategic plan

Abstract The private higher education (PHE) is now starting to intensify the use of IT (Information Technology). Often the activities conducted under conditions that are not ideal due to budget constraints. To develop the ICT (Information Communication Technology) systems in legalization efforts of private higher education is to encourage the use of software based on open-source software, the integration of all cross-data unit services within a more efficient organization to support the Decision Support System database and data warehouse, a service that is prioritized for all and access to all levels and access universally. Integration of both horizontally (between systems managing different areas) and vertically (following a structural hierarchical relationship). Placed within the framework of a holistic, top-down approach. Developed and implemented naturally, from the base (database infrastructure) grew (application and access). The PHE development done gradually evolved in different time frames, focus on procurement and IT facilities, both hardware and software. Focus on how to manage IT-based services. Focus on how to bring IT and internal seep into the organizational structure, and ultimately the transformation of the organization and the people in it. Stages of analysis can be defined with the Use Case diagrams, divided into several main modules.
Impacts and Challenges of Cost-Effective Approaches Using Hybrid Cloud Infrastructure Model in Business Analytics

The cost-effective methodology and its implementation are the primary approaches towards cost computing to bring effectiveness with the proper requirements and provide the proper solution. This chapter focuses on the discussion about the cost-effective method using cloud infrastructure model for building and management of on-premise with the off-premise cloud service provider in business analytics. This chapter also elaborates the methodology undertaken and design considerations for implementation of cloud infrastructure with non-virtualized and on-premise infrastructure environment. The experiment using YGCIS (YG-cloud infrastructure solution) methodology is built for business analytics platform where infrastructure and its resources play a vital role. The cost-effective approach for total cost ownership (TCO) is implemented using YGCCS (YG-cost computing solution) framework. Thus, the solution obtained after implementing the above frameworks increases ROI % and reduces the TCO, impacting the business analytics needs.
Study of Precipitation Influence on the Sizing of PV Installation for Irrigation System

The paper presents a study how the precipitations can influence on the rated power of photovoltaic installation for irrigation system. A previously proposed methodology for sizing of renewable energy based power supply is improved to take into account statistical information for the precipitations in the considered installation site. To estimate the influence of this factor the new methodology is applied to 7 different sites in Bulgaria, popular with favorable conditions for the considered agricultural crop - wines. The results show that if the precipitations are taken into account the installed PV system can be reduced even in the case of wines that need irrigation in months with occasional rains - from June to August.
A Noval Fuzzy-Based Artificial Bee Colony Algorithm for Medical Records Classification

Data mining is the core step of a broader perspective known as the knowledge discovery. Many nature-inspired algorithms have been designed for this pursuit. Many of them are functioning with higher classification accuracy. The artificial bee colony (ABC) is one of them. However, it has been visualized that subsequent performance enhancement can be rendered in this algorithm by assimilating the fuzzy concept with exiting state-of-the-art ABC algorithm. Therefore, attempt has been made to construct a fuzzy-based ABC algorithm. The performance of proposed Fuzzy-ABC is evaluated with Latent Dirichlet Allocation (LDA) algorithm for the classification of medical records. Three frequently used datasets are taken for this purpose. Experimental analysis reveals the fact that the proposed Fuzzy-ABC renders a superior performance over the LDA technique considered in the experimentation. Further, the proposed algorithm is also validated using fivefold cross-validation.
Research on Big Data Classification Technology Based on Deep Learning

Based on the current digital industry background, the production processes become centralized and closely related to each other. If industrial production fails, it will bring great damage to it, which makes it more and more important to accurately identify the status of industrial equipment and repair it in real time by collecting time series data with industrial sensors. However, due to the large number, variety and high-frequency sampling of sensors, industrial big data has certain complexity, i.e. high spatial dimension, complex logical relationship, changeable rules, large amount of data and so on. At present, end-to-end algorithms of deep learning are widely used in many fields. From the perspective of application of deep learning, this paper studies its application in industrial big data time series classification, and analyzes the characteristics of industrial data and the challenges of industrial time series classification from three aspects: accurate classification, efficient classification and incremental learning.
A Novel Authentication Method for Password Encryption

Data hacking has become one of the anxiety issue in this modern era. Therefore, we have to prevent this illegal data hacking, theft at any cost. For ensuring data integrity, there are many methods including backups, encryption, access control, etc. We have presented an encryption methodology without merging the existing methods. By using the number conversion system and a defined mathematical calculation equation is the key to encrypt the password of the users. The password and date of birth are inputted by the users during the signup process. The encryption of the passwords has been stored into the database and manual calculation for the same cardinalities provides the same result. We have achieved an accuracy of 96% which is good as well as promising. Encryption is using in every sector to protect data and it has a wide range of future to work with it.
Shannon unicity distance, ε-secrecy criterion and Holevo information in quantum cryptography

Abstract In this paper, we consider the relationship between C Shannon’s unicity distance, which is determined by the number of symbols of the encrypted message required to uniquely find the encryption key of the cryptosystem in information-theoretical sense, ε -secrecy criterion in quantum cryptography and Holevo information. A lower bound for the unicity distance for ε -secret keys in quantum cryptography is obtained. The relationship between unicity distance, ε -secrecy criterion in quantum cryptography and Holevo information is demonstrated.
An Exercise in a Non-classical Semantics for Reasoning with Incompleteness and Inconsistencies

Reasoning in the presence of inconsistencies and in the absence of complete knowledge has long been a major challenge in artificial intelligence. In this paper, we revisit the classical semantics of propositional logic by generalising the notion of world (valuation) so that it allows for propositions to be both true and false, and also for their truth values not to be defined. We do so by adopting neither a many-valued stance nor the philosophical view that there are ‘real’ contradictions. Moreover, we show that satisfaction of complex sentences can still be defined in a compositional way. Armed with our semantic framework, we define some basic notions of semantic entailment generalising the classical one and analyse their logical properties. We believe our definitions can serve as a springboard to investigate more refined forms of non-classical entailment that can meet a variety of applications in knowledge representation and reasoning.
TRETA - A Novel Heuristic Based Efficient Task Scheduling Algorithm in Cloud Environment

Cloud computing is a computing platform that allows users to access various kinds of computing services over the internet. Cloud provides on-demand, scalable and highly available resources on pay-per-usage subscriptions. Cloud is an optimum solution for executing a large number of different size tasks as for the computing capability it offers. Task scheduling is one of the major open challenges that need to be addressed. The Task scheduling problem in the cloud is known to be an NP- complete problem. Hence heuristics can be used to get an optimal solution. There have been many heuristics proposed for the task scheduling problem in the cloud. None of them has considered the total execution time of the virtual machine as a factor for finding a better schedule. In this paper, we proposed a new task scheduling algorithm named Total Resource Execution Time Aware Algorithm (TRETA) which takes into account the total execution time of computing resources in obtaining an optimal schedule. The algorithm is compared with Min-Min, Min-Max, FCFS, and MCT heuristics for Makespan, Degree of Imbalance and System Throughput. The proposed algorithm shows a significant amount of improvement in Makespan compared to other heuristics. The algorithm also outperforms other heuristics with respect to System Throughput and Degree of Imbalance which results in better workload distribution among the cloud resources.
Noise Estimation-based Method for MRI Denoising with Discriminative Perceptual Architecture

In this paper, we investigate the case for the discriminative perceptual architecture in magnetic resonance imaging Rician noise removal. While the existing denoising methods based on noise estimation have achieved the superior performance under the synthetic situation, it fails to remove certain real noise and lost some texture details as the overfitting noise estimation. In order to improve the noise estimation based methods for MRI denoising, we propose to use a perceptual feature extractor to calculate the perceptual loss and the discriminator structure of WGAN to reduce the noise distribution divergence. The experiment demonstrates superiority of our proposed method in removing MRI noise and reserving good texture details.
Transmission of radiology images over an Unsecure Network Using Hybrid Encryption Schemes

In this paper., we propose a hybrid encryption scheme to transmit the medical image dataset securely in radiology networks. The proposed methodology uses the RSA (Rivest-Shamir-Adleman) encryption technique., XOR technique, and the digitally reconstructed Radiograph (DRR) image from the 3D volume of MRI scan images. As a first step, the volume of interest (VOI) was segmented and then computed the DRR image on the segmented volume in the sagittal direction. The pixels of the DRR image was XORed with all the image slices. All the images and the DRR image were encrypted separately using the RSA technique and transmitted. At the receiver, the XOR was applied to all the received images, the original slices were retained, VOI was segmented again, and the DRR was recomputed. Now, the received DRR and the recomputed DRR were compared for the changes in the image content through histogram comparison, MSE, and Mean absolute deviation. The data integrity violation was tested by adding an image, deleting an image, and modifying the pixels of the image before sending it. The method was applied to fifty (n=50) samples. In all the above test cases performed, the method identified the data integrity violation correctly.
Improvements to RSA Key Generation and CRT on Embedded Devices

RSA key generation requires devices to generate large prime numbers. The naïve approach is to generate candidates at random, and then test each one for (probable) primality. However, it is faster to use a sieve method, where the candidates are chosen so as not to be divisible by a list of small prime numbers \(\{p_i\}\).
Simulation Study using Average Difference Algorithm to Analyze the Outlierness Degree of Spatial Observations

Attribute values are the main elements in calculating degree of outlierness of spatial objects. The problem arises when the spatial outliers with extreme values are the nearest neighbors of a central object. In this study, several scenarios are simulated to verify the effect of spatial outliers' extreme values to the degree of outlierness of its nearest neighbors, based on Average Difference Algorithm. The results confirmed the effect can lead to falsely detected spatial outliers. The algorithm detect the true spatial outliers correctly if their values are three sigma away from the mean attribute values of its nearest neighbors.
CSASN: Learning Complementary Spatial-Aware Siamese Networks for Visual Object Tracking

Recently, Siamese networks have drawn great attention in visual object tracking due to their balanced accuracy and speed. However, most such trackers inherently learn the general object representation which cannot discriminate targets from similar distractors robustly, thus making it easier to drift. To tackle this challenging problem, we propose a novel architecture named Complementary Spatial-Aware Siamese Network (CSACN), which consists of two branches. In the Siamese branch, a Siamese network with a Correlation Filter(CF) layer cascaded is adopted for convolutional feature extraction and online tracking process. In the Complementary Spatial-Aware(CSA) branch, target-specific features are extracted from Histogram-related Probability Module (HPM) and Edge-related Template Module (ETM) to further boost the tracking capacity. Especially, a novel Spatial-Aware Module (SAM) is employed to penalize large inter-frame movements, thus correcting the object location effectively. Finally, an adaptive update strategy is proposed to alleviate the contamination of training samples. Extensive evaluations on OTB-2013, OTB-2015, VOT-2016 and VOT-2017 datasets demonstrate that CSASN is very promising for various challenging scenarios compared with several state-of-the-art trackers while running at 45 frames per second.
A Novel Weakly-Supervised Proposal Method Based on Vector Perceptron and Unbalanced Action-Background Self-supervised Boundary Inferring

In weakly-supervised temporal action detection, only the category labels of action instances could be used for training. Without any temporal boundary annotations in an untrimmed video, it is a huge challenge to propose the starter and the end of an action (named as "the proposa"). In this paper, we try to focus on the temporal differences of visual contents which may change significantly when motion occurs and carry the information of temporal boundaries. First, a motion-propagating feature vector (MPV) is designed to describe different kinds of temporal differences in a video. Second, we propose a temporal difference perceptron (TDP). According to the MPV, TDP computes an attention to each video snippet to infer action areas. However, the TDP only concentrates on the most discriminate action areas and the attentions fail in slow actions. To tackle these, an unbalanced action-background self-supervised loss (USSL) function is custom to tune the distribution of activated change areas. With the unbalanced constraint, all forward changes and some backward change action regions are activated. And the self-supervised attention labels force the model to widen the gap between background and action in feature space, contributing to boundaries inferring. Our method achieves comparable performance with the state-of-arts, which outperforms STPN, an algorithm without temporal difference message, more than 2% mAP on THUMOS14, and is 20.8% (mAP@tIou=0.5) higher than UntrimmedNets on ActivityNet v1.2 which used temporal difference information roughly.
A Study on VCM Anchor Generation for Object Segmentation

In the MPEG-VCM framework, an efficient video coding technique while maintaining task performance such as object segmentation is required. In this paper, we introduce the MPEG- VCM framework and show some results of VCM anchor generation on Cityscape dataset in the object segmentation task.
Burmese Text Images Classification of Based on Neural Network

The first stage of Burmese and Russia's bilingual scene text detection and recognition is the detection of Burmese and Russia's scene text. The detection results are mainly divided into three categories: successfully detected regions of Burmese, Russian text, and non-words regions with failed predictions. If the detected text image results are accurately classified, then the non- text images should be filtered in the recognition phase, meanwhile, the Burmese and Russia text images can be identified by using different classifiers, such a procedure can reduce the complexity of classification and recognition of two different characters by one recognition model. Accurate classification of Burmese and Russian text images is mattered. This paper researches the classification of Burmese and Russian text image and non-text images by using convolutional neural networks. We perform a series of exploration about the classification accuracy of Burmese and Russian text images and non-text images and compare the accuracy with the classification results based on the transfer learning then analyze it. The results show that using a 7-layer convolutional neural network has reached saturation, and increasing the network depth does not improve the results, which provides reference values for Burmese and Russia text image classification.