Reviewerly/exHarmony huggingface dataset:
CRIST900: A Fully-Labeled Natural Image Dataset for Multi-Operator Content Aware Image Retargeting

A fully-labeled image dataset provides an exclusive resource for reproducible analysis, investigation inquiries and data analyses in different research computational fields like machine learning, computer vision and deep learning machine intelligence. This research paper present a large scale fully-labeled natural image dataset for Multi-Operator content aware image retargeting techniques. The image dataset is feely available for image processing research field. The current research natural image dataset entitled CRIST900, it include 900 natural images and uses for content aware image retargeting. The proposed CRIST is an image retargeting Multi-Operator method called Content Retargeting Image reSizing Technique. The proposed image resizing method has three phases, image object boundary identification, the image objects feature importance visual saliency map generation and Multi-Operator techniques for efficient retargeting of image objects. The Multi-Operator retargeted image quality assessment was done by different subjective and objective image quality assessment matrices. The experimental result shows that the proposed approach can attain better result than the traditional methods and content aware state-of-the-art image retargeting techniques. The research dataset is publicly available at https://sites.google.com/view/abhayadevmalayil/home .
Query expansion using Wikidata attributes’ values

One of the well known techniques that are used to increase the relevancy of user queries is query reformulation through the expansion of queries.Also, Linked data are being used in various fields and available for different domains.This paper aimed at improving Information Retrieval results using Linked data from Wikipedia in general and Wikidata in particular to expand the queries with attributes" values.The experiments that we have done, using Medline collection and Indri search engine to compare our suggested approach with baseline, lead to improvements in precision at different ranks.
Blockchain-Based Resource Syndicate

Blockchain was introduced as an incorruptible distributed data structure for financial transactions. Its contents are intrinsically verifiable, it sustains its state over multiple nodes, and its property of consistent replication can be used to build an automated structure to control computational resources.
Stochastic sub-sampled Newton method with variance reduction

Stochastic optimization on large-scale machine learning problems has been developed dramatically since stochastic gradient methods with variance reduction technique were introduced. Several stochastic second-order methods, which approximate curvature information by the Hessian in stochastic setting, have been proposed for improvements. In this paper, we introduce a Stochastic Sub-Sampled Newton method with Variance Reduction (S2NMVR), which incorporates the sub-sampled Newton method and stochastic variance-reduced gradient. For many machine learning problems, the linear time Hessian-vector production provides evidence to the computational efficiency of S2NMVR. We then develop two variations of S2NMVR that preserve the estimation of Hessian inverse and decrease the computational cost of Hessian-vector product for nonlinear problems.
A Divide-and-Conquer Strategy for Adaptive Neuro-Fuzzy Inference System Learning Using Metaheuristic Algorithm

Adaptive neuro-fuzzy inference system (ANFIS) has produced promising results in model approximation. The core of ANFIS computation lies in the training of its parameters. Metaheuristic algorithms have been successfully employed on ANFIS parameters training. Conventionally, a population individual in metaheuristic algorithm, considered as ANFIS model with candidate parameters, is evaluated for its fitness on complete training set. This makes ANFIS parameters training computationally expensive when dataset is large. This paper proposes divide-and-conquer strategy where each population individual is given a piece of dataset instead of complete dataset to train and evaluate ANFIS model fitness. The proposed ANFIS training approach is evaluated on accuracy on testing dataset, as well as, training computational complexity. Experiments on several classification problems reveal that the proposed methodology reduced training computational complexity up to 93%. Moreover, the proposed ANFIS training approach generated rules that achieved better accuracy on testing dataset as compared to conventional training approach.
A Comparative Study on k-means Clustering Method and Analysis

A study of three clustering methods using four different cluster validity metrics is being presented here. We have discussed the clustering methods and made an analysis. We have given the mathematical formation of four cluster validity measures. From the experimental outcomes, indications regarding the optimal validation method, as well as, optimal clustering method are being presented. Choice of preferable clustering technique is presented after getting outcomes using real-world data sets.
Velocity on the Web - a PhD Symposium

It is a streaming world: a new generation of Web Applications is pushing the Web infrastructure to evolve and process data as soon as they arrive. However, the Web of Data is not appealing to the growing number of Web Applications demanding to tame Data Velocity. To solve these issues, we need to introduce new key abstractions, i.e., stream and events. Moreover, we need to investigate how to identify, represent and process streams and events on the Web. In this paper, we discuss why taming Velocity on the Web of Data. We present a Design Science research plan that builds on the state of the art of Stream Reasoning and RDF Stream Processing. Finally, we present our research results, for representing and processing stream and events on the Web.
Detecting Illicit Drug Ads in Google+ Using Machine Learning

Opioid abuse epidemics is a major public health emergency in the US. Social media platforms have facilitated illicit drug trading, with significant amount of drug advertisement and selling being carried out online. In order to understand dynamics of drug abuse epidemics and design efficient public health interventions, it is essential to extract and analyze data from online drug markets. In this paper, we present a computational framework for automatic detection of illicit drug ads in social media, with Google+ being used for a proof-of-concept. The proposed SVM- and CNN-based methods have been extensively validated on the large dataset containing millions of posts collected using Google+ API. Experimental results demonstrate that our methods can efficiently identify illicit drug ads with high accuracy. Both approaches have been extensively validated using the dataset containing millions of posts collected using Google+ API. Experimental results demonstrate that both methods allow for accurate identification of illicit drug ads.
Recommendation of Item Category Based on Random Walk Model

Recently, with the development of e-commerce and the enrichment of the online shopping, the explosion of commodity information results in the problem of information overload. It is an effective way to employ recommender system, helping users find their satisfied goods. However, the existing recommendation systems face two problems. First, they can't achieve good performance due to lack of adaptation. On the other hand, the existing recommendation systems can not realize the flexibility to find potential users for any products, only unilaterally predicting products for users. In this paper, we propose a method for recommendation of item categories based on improved random walk model. We construct a graph combining the users' behaviors with the objective knowledge of items. An improved graph-based algorithm is employed to predict the items or customers score. The proposed method can find possible purchases for users, as well as finding potential customers for goods. The experiments were evaluated on Meituan-Dianping and Retailrocket datasets both demonstrated that our methods gains a higher hit ratio of 0.140 and 0.159 over the existing recommendation methods, when adopted the HR@k evaluation method (k=5).
Research on Assisted Generation Method of Emergency Management Structured Case for Natural Text

At present, most of the structured emergency case generation relies on the manual summary of relevant emergency management personnel which is labor intensive and inefficient. To solve this problem, this paper proposes an assisted generation method of emergency management structured case for natural text. The method uses natural language processing technology and some machine learning algorithms to automatically generate structured cases from common accident investigation reports. It reduces the dependence on the artificial when the structured cases are generated. Those generated structured cases are easy to manage and and has a clear structure, and they can be used in teaching and research, as well as for decision-making. Therefore, it has important practical significance and research significance.
IEEE P4001: Progress towards a hyperspectral standard

Hyperspectral imaging is an innovative and exciting technology that holds incredible diagnostic, scientific and categorization power. Current industry innovation is a testament to the creative power and imagination of the diverse community seeking to optimize this technology. However, fundamental instrument performance is not consistently well characterized, well understood or well represented to suit distinct application endeavors or commercial market expectations. Establishing a common language, technical specification, testing criteria, task-specific recommendations and common data formats are essential to allowing this technology to achieve its true altruistic and economic market potential. In 2018 the IEEE P4001 was formed to facilitate consistent use of terminology, characterization methods and data structures. This talk is a progress report to inform the hyperspectral community of the status of the work to date, the interconnection with other standards and outline the roadmap.
Lossy Identification Schemes from Decisional RSA

Lossy identification schemes derive tightly secure signature schemes via the Fiat–Shamir transformation. There exist several instantiations of lossy identification schemes by using several cryptographic assumptions. In this paper, we propose a new construction of the lossy identification scheme from the decisional RSA assumption which are introduced by Groth. Our lossy identification scheme has an efficient response algorithm because it requires no modular exponentiation.
Face Recognition Based on FP-Growth Improved LBP Operator

The basic features extracted by the basic LBP operator are not complete. It only analyzes the symbolic values of local differences. How to improve the features extracted by the LBP operator and how to effectively express the information that the basic LBP operator fails to express to achieve a better recognition rate has not yet been solved. By selecting FP-tree, the grey value of the image is frequently analyzed, and then the feature of LBP is used to extract features, so as to obtain a more comprehensive local feature of the extracted facial image.
A data‐driven support strategy for a sustainable research software repository

Summary We describe a sustainable strategy to support a large number of researchers with widely varying scientific software needs, which is a common problem for most centralized Research Computing Centers on university campuses. Changes in systems and hardware, coupled with aging software, often necessitates re‐compilation of existing software. The naive approach of re‐compiling all of the existing packages is not only counterproductive but may also become unrealistic, especially for small support teams such as Georgia Tech's PACE Team. Instead, we analyze job scheduling data to identify actively used software, then rank, and distribute them in three support tiers, which define the level of support we provide. The distribution of software into multiple tiers is a non‐trivial problem. We use a heuristic ranking algorithm that uses four metrics, namely the number of users, groups, jobs, and their collective runtimes. The results revealed a surprisingly small subset of software that is sufficient to support a very large portion of the overall research computing activity on campus. This approach allows us to make data‐driven strategic technical and policy decisions to provide high‐quality support for the software that really matters and sustain these services with a relatively small team in the long term.
The Problems of Teaching and Learning in a Cross-Cultural Environment

In modern society, tutors often interact with a multicultural student’s audience in the traditional or online format. The majority of tutors emphasize the problem of constructive knowledge transfer in a multicultural learning environment as the main problems in this context, in addition to cognitive, communication, and psycho-pedagogical specifics. The development of education that is receptive to cultures needs not only specialists in different subjects, but also teachers who have knowledge in the cross-cultural differences sphere. These days, training courses and programs including distance learning are monocultural, that is, do not fully meet the needs of students in information society. Thereby, the main question is how to build constructive education in the cross-cultural education context. We claim that nowadays, there is a necessity of training the specialists with a developed cultural intellect. In this paper, we develop some ways of optimizing the education process in a cross-cultural environment.
Collaborative Explanation of Deep Models with Limited Interaction for Trade Secret and Privacy Preservation

An ever increasing number of decisions affecting our lives are made by algorithms. For this reason, algorithmic transparency is becoming a pressing need: automated decisions should be explainable and unbiased. A straightforward solution is to make the decision algorithms open-source, so that everyone can verify them and reproduce their outcome. However, in many situations, the source code or the training data of algorithms cannot be published for industrial or intellectual property reasons, as they are the result of long and costly experience (e.g. this is typically the case in banking or insurance). We present an approach whereby individual subjects on whom automated decisions are made can elicit in a collaborative and privacy-preserving manner a rule-based approximation of the model underlying the decision algorithm, based on limited interaction with the algorithm or even only on how they have been classified. Furthermore, being rule-based, the approximation thus obtained can be used to detect potential discrimination. We present empirical work to demonstrate the practicality of our ideas.
Deep Neural Networks Applied to the Dynamic Helper System in a GPGPU

This paper presents a study about the use of two deep neural networks (MLP and LSTM) in the Dynamic Helper System (DHS), which is a system that only exists in the EFC railway. The DHS assists a fully loaded train in sections with slopes and climbs. Therefore, this work shows that it is possible to predict the next action of the DHS using a classification algorithm, specifically, deep neural networks. The training and testing dataset is a real one, and the training task was performed using PyCUDA, Keras, and TensorFlow in a General Purpose GPU. Results using a real-world dataset indicate that deep neural networks can reach an accuracy above 99% and precision about 81% on predicting the next action of the DHS.
Headline hype disguises security failings

The world is not short of headlines about cyber security – the latest breach, the latest malware, the latest vulnerability. The world has become vastly better educated about the risk – from a position 10 years ago where most board members would respond to the mention of cyber security with “I think there's a guy called Bob in IT who deals with that” to the position today where boards routinely identify cyber security as their number one business risk. The world is not short of headlines about cyber security incidents and most organisations have become better educated about the risk. Among security professionals, however, the news flow feeds the risk of ‘shiny object syndrome’ – a focus on the latest threat or security tool. Many organisations have invested in swathes of new security software, establishing a defence in depth that layers product upon product. But software contains vulnerabilities and that applies to security software too. However, there are technologies that offer us a new way forward, argues Henry Harrison of Garrison.
Ring Theoretic Key Exchange for Homomorphic Encryption

We propose a key exchange protocol that works in a polynomial ideal setting. We do this so that the key can be used for a homomorphic cryptography protocol. The advantage of using key exchange over a public key system is that a large proportion of the process needs to be carried out only once instead of needing a more complicated encryption function to use for each piece of data. Polynomials rings are an appropriate choice of structure for this particular type of scheme as they allow universal computation. This paper will examine how we can perform computation correctly on cipher texts and address some of the potential weaknesses of such a process.
Combating Packet Loss in Image Coding Using Oversampling, Irregular Interpolation and Noise Shaping

Diversity "multiple description" (MD) source coding promises graceful degradation in the presence of an unknown number of erasures in the channel. A simple scheme for the case of two descriptions consists of oversampling the source by a factor of two and delta-sigma quantization. This approach was applied successfully to JPEG-based image coding over a lossy packet network, where the interpolation and splitting into two descriptions is done in the discrete cosine transform (DCT) domain. The extension to a larger number of descriptions, however, suffers from noise amplification whenever the received descriptions form a nonuniform sampling pattern. In this work, we examine inter and intra-block interpolation methods and show how noise amplification can be reduced by optimizing the interpolation filter. Specifically, for a given total coding rate, we demonstrate that an irregular interpolation filter minimizes the average distortion over all (K out of N) patterns of received packets, ("side receivers"). We provide experimental results comparing low-pass (LP) and irregular interpolation filters for the side receivers and the all-N central receiver. We further examine the effect of noise shaping on the trade-off between the central and side distortions.
Brain-computer fusion artificial intelligence system based on transfer learning

In order to solve the problems faced in the transfer learning of artificial intelligence system modeling technology, a new method of TSK transfer learning fuzzy system was proposed to enhance knowledge transfer. The two key problems of the precursor learning and the post learning of the TSK type tr ansfer fuzzy system were solved by using this method. And also a novel transfer fuzzy clustering method was proposed for solving the problem. At the same time, a post learning mechanism was proposed to enhance the ability of knowledge transfer which effectively improved the performance of the final model. The experimental results showed that the proposed method integrated the transfer clustering and the transfer fuzzy system modeling successfully, making the modeling process of the fuzzy system more intelligent with better learning ability. In addition, the proposed method provided a new research idea for the development of transfer learning in the field of intelligent modeling.
VLSI Architectures for Jacobi Symbol Computation

Jacobi symbol calculation is one of the major computation steps for certain cryptographic algorithms. In this paper, we propose a bit-sliced VLSI architecture for the same, essentially tailored for FPGA implementations that exploits the fast carry chain fabric for realizing the circuit. The architecture was essentially conceived through appropriate configuration of the target FPGA specific primitives to achieve an optimized realization in terms of delay. Our implementation outperforms behaviorally modeled circuit having similar functionality using higher levels of design abstraction, with respect to speed.
Mackey-Glass Chaotic Time Series Prediction Using Modified RBF Neural Networks

The characteristics of a nonlinear dynamical system within chaotic system is more intensely studied recently, due to many real-world applications of the nonlinear chaotic system are increasing. For characterizing the ordinary system, usually the relationship between the linearity and the nonlinearity of parameters in the system is needed to be firstly derived, however, creating the mathematical model of the real chaotic system is still a problematic since insufficient basic physical phenomena should be analyzed. Hence, artificial neural networks approach that performed based on nonlinear mathematical model is quite adequate to be used to analyze the chaotic phenomena within the system. Solving the multi-step ahead prediction problem of time series chaotic system is one of the top challenging issues, especially on how to obtain a higher prediction rate. In this paper, a modified Radial Basis Function Neural Network (RBF-NN) is developed and be tested for predicting the future state of a Mackey-Glass equation as the chaotic system. Results experiments show that using training testing paradigm of 50%:50%, the calculated of confidence level accuracy of the neural-predictor system is satisfied for up to 30-steps ahead prediction.
Automatic Detection of Argument Components in Text Using Multinomial Nave Bayes Clasiffier

Arguments are often found in various text data, for example in news, essays and articles. Argumentation Mining is a method that automatically identifies argument structures in text documents. This argument structure consists of several components that are very useful for information retrieval and processing information. In this study, a model will be built to auto-matically detect the component of argument, by using naive bayes classifer multinomial, the model will classify argument components into two classes, namely claim and premise. The evaluation uses k-fold cross validation. The most optimal result of this study is the average accuracy of 70.39 % and the average f1-score of 80.42 % with feature extraction, preprocessing and weighting words.
Reliability and Economic Efficiency in Power System with PV Considering Import Fuel Price Transition

In conventional power systems, large power sources such as thermal, hydro, and nuclear power plants supply the vast majority of the electricity. However, in future power systems, the installed capacities of renewable energy sources such as wind power and photovoltaic generation (PV) systems will increase, and these sources will supply a large volume of electricity. The potentially frequent occurrence of supply-demand imbalances in future power systems is a concern. Therefore, coordinated operation with supply-demand balancing between large power sources and renewable energy sources is necessary. In this study, we focus on operation with supply-demand balancing and PV generation, considering both economic efficiency and reliability. We evaluate the impact of import fuel price transitions on supply and demand in a future power system (for the assumed period of 2030-2050) in which a large PV generation source is installed. We conducted numerical simulations on a bulk power system model. We consider both the import fuel price and PV-generation installed capacity as parameters.