Reviewerly/exHarmony huggingface dataset:
Comparing the Effort Estimated By Different Models

Management of project software starts with a collection of activities referred to as project planning procedure. A company's team must decide the work to be done, the resources to be reorganized and a time from beginning of the calculation until project starts. Following completion of these activities, the program team will set up a set of projects that will assign program development tasks, identify key milestones, identify responsibilities for each task and identify related dependencies among participants that may have a significant impact on progress. There is usually no full precise estimation process, but in this research we have tried to find the best programming methods to find the best estimate of programming. Effort estimation is one of greatest objection of STLC. It is platform for planning, estimating and preparing effort for project. This paper demonstrates model with a purpose of depicting bias variation and an accuracy of the technology of an enterprise test attempt estimates concluding the function of Cobb-Douglas (CDF), Neuro fuzzy approach, and Genetic methods. The purpose of this review is to present an analysis of principles to minimize software costs and to explain how these concepts are applied to general system divisions. We deliver simple algorithms namely-Cobb Douglas, Genetic Algorithms, and Adaptive Neuro Fuzzy Approach to decide which algorithm is best suited to finding the best estimates as accurate as possible. The best outcomes they have been found in Neuro Fuzzy Approach. The Neuro Fuzzy has highest accuracy to be found, but the Genetic Algorithm was better than Fuzzy Logic, the worst compared to Cobb Douglas and Genetic Algorithms.
Big data: and the micropolitics of entanglement in the Earth’s becoming

This paper addresses the entanglements of the earth’s becoming in a multi site cross hemisphere study of the infinite moments of young children’s world making, recorded in multimodal videos, still images, and children’s productions. The postqualitative project was informed by new materialist and posthuman theorising. The researchers conducted their research at seven different early learning centres in NSW, Queensland, Victoria, and Finland. At the end of the project the researchers came together to make sense of the data across all sites and realised that their postqualitative data was BIG and that their ‘little big data’ was generated in the context of the increasing impact of ‘big Big Data11 We refer to our ‘little big data’ throughout this paper by using lower case for big data in contrast to upper case for ‘big Big Data’ to differentiate between the two..’ We discuss our approaches to posthuman and new materialist big data in order to create a conversation between the two and to highlight the alternative methodological processes we developed to make sense of large bodies of postqualitative data.
Performance Enhancement of Adaptive Image Contrast Approach by using Artificial Bee Colony Algorithm

Image Contrast enhancement emphasizes the information present in low dynamic range of grey scale image. Good quality image facilitates increased extraction of information from them. The objective of this paper is to find the optimal values of parameters using Artificial Bee colony algorithm, which when applied on the transformation function enhances the grey scale image. We propose a new fitness function for evaluating contrast of image, that will guide the Artificial Bee colony algorithm into picking the optimal values. The performance of the proposed method is quantitatively computed using peak signal to noise ratio (PSNR), SSIM(Structural Similarity Index), SNR(Signal to Noise Ratio), MSE(Mean Squared Error) with existing methods.
Hybrid Helmholtz machines: a gate-based quantum circuit implementation

Quantum machine learning has the potential to overcome problems that current classical machine learning algorithms face, such as large data requirements or long learning times. Sampling is one of the aspects of classical machine learning that might benefit from quantum machine learning, as quantum computers intrinsically excel at sampling. Current hybrid quantum-classical implementations provide ways to already use near-term quantum computers for practical applications. By expanding the horizon on hybrid quantum-classical approaches, this work proposes the first implementation of a gated quantum-classical hybrid Helmholtz machine, a gate-based quantum circuit approximation of a neural network for unsupervised tasks. Our approach focuses on parameterized shallow quantum circuits and effectively implements an approximate Bayesian network, overcoming the exponential complexity of exact networks. In addition, a new balanced cost function is introduced, preventing the need of millions of training samples. Using a bars and stripes data set, the model, implemented on the Quantum Inspire platform, is shown to outperform classical Helmholtz machines in terms of the Kullback–Leibler divergence.
Analysis of Weibo Comments Based on SVM and LDA Models

Nowadays, Sina Weibo is an important way for people to voice their opinions in China. With the development of social media computing, we can find out public opinions of an event by analyzing Weibo comments using computational methods. In this paper, we focus on a hospital event which happened in China. First, we collect comments about the event from Sina Weibo blogs of 4 different kinds of bloggers including traditional media, medical media, Internet celebrity and actor self-media. Then we estimate the sentiment of the comments by making use of Support Vector Machine (SVM) model. Finally, we extract topics from the comments by using the Latent Dirichlet Allocation (LDA) topic model for the purpose of explaining the reason of different sentiment for each blogger.
An Enhanced Traffic Abnormality Detection System for Vehicle Characteristics Tracking using Radar Signals

Due to urbanization and population explosion in India, the number of vehicles on the road every day is increasing at an unprecedented rate. This indirectly calls for increase in rate of road accidents and fatality rate. With budding economic trends and shape-shifting growth plot of urban world, the need to improve consumer safety all together has been a major issue in hand for the transportation magnet industries. Inefficient transport system, meagre congestion management and reckless drivers with no regard to human life have been a major stake around the epicenter of all snags of road accidents. Thus, there is a crucial need to inculcate technological signatures to uplift the current backlogs transportation industry is facing, by making it more accessible, environmentally friendly, faster, comfortable, safe and affordable without compromising the future needs. Wrong way driving on busy roads and highways can have catastrophic outcomes. If a wrong way driver is not detected and dealt with instantly, they will be endangering the lives of themselves and other road users. This article therefore attempts to detect perilous conditions such as vehicles approaching in oncoming lane traffic and slow-moving vehicles and inform and aware the drivers who is forthcoming in the same lane.
Modelling and forecasting art movements with CGANs

Conditional generative adversarial networks (CGANs) are a recent and popular method for generating samples from a probability distribution conditioned on latent information. The latent information often comes in the form of a discrete label from a small set. We propose a novel method for training CGANs which allows us to condition on a sequence of continuous latent distributions f (1) , …, f ( K ) . This training allows CGANs to generate samples from a sequence of distributions. We apply our method to paintings from a sequence of artistic movements, where each movement is considered to be its own distribution. Exploiting the temporal aspect of the data, a vector autoregressive (VAR) model is fitted to the means of the latent distributions that we learn, and used for one-step-ahead forecasting, to predict the latent distribution of a future art movement f ( K +1) . Realizations from this distribution can be used by the CGAN to generate ‘future’ paintings. In experiments, this novel methodology generates accurate predictions of the evolution of art. The training set consists of a large dataset of past paintings. While there is no agreement on exactly what current art period we find ourselves in, we test on plausible candidate sets of present art, and show that the mean distance to our predictions is small.
Stream Cipher Based on Chaotic Maps

Information Security has still the aim of many agencies and organisations. They have great attention to cryptosystems to ensure the security of their information. So, scientists and researchers had proposed many cryptosystems and improve their performance via new methods and techniques. Most recent studies depended on the chaotic map with cipher systems which had gained most researchers to improve its security and robustness. The continued studies had mentioned several methods to strength these chaotic based ciphers and the randomness of its keystream generation. Some of the studies had proposed successful cipher techniques while others had not or advised further improvement. Another side, this paper tests chaotic maps which had used in the cipher algorithms to evaluate its randomness. In general, this paper gives a review of recent stream cipher based on chaotic maps. It also shows the randomness evaluation of chaotic maps. The survey recommended that Chen map is the most random method.
Multiple mini-robots navigation using a collaborative multiagent reinforcement learning framework

Piyabhum Chaysri, Konstantinos Blekas* & Kostas Vlachos Department of Computer Science and Engineering, University of Ioannina, Ioannina, GreecePiyabhum Chaysri received the B.Sc. degree in Computer Science in 2016 and received the M.Sc. degree in Technologies and Applications in 2018, both from the University of Ioannina, Ioannina, Greece. He currently continues his Ph.D. research in Reinforcement Learning Agents in Robotic Applications at the same university. His research interests include machine learning, intelligent agents, reinforcement learning, navigation and control of robotic platforms as well as designing and construction of robotic mechanisms.Konstantinos Blekas received the Diploma degree in Electrical Engineering in 1993 and the Ph.D. degree in Electrical and Computer Engineering in 1997, both from the National Technical University of Athens. He is currently associate professor at the Department of Computer Science & Engineering, University of Ioannina, Greece. He has co-authored more than 90 refereed journal and conference articles. His research interests include machine learning, pattern recognition, intelligent agents and reinforcement learning with applications to robotics and autonomous systems, computer vision, and medicine.Kostas Vlachos received the B.Sc. degree in electrical engineering from the Technical University of Dresden, Dresden, Germany, in 1993. He received from the National Technical University of Athens, Athens, Greece, the M.S. (2000) and Ph.D. (2004) degrees in the area of Automatic Control and Robotics respectively. From 1996 to 1998, he worked as a Software Analyst in INTRACOM S.A. From 2007 to 2013, he was a visiting Lecturer at the Mechanical Engineering Department, University of Thessaly, where he taught courses in the areas of Control Systems, and Robotics. Currently, he is an Assistant Professor with the Department of Computer Science and Engineering, University of Ioannina. Research interests: haptic mechanisms, medical simulation, microrobotics, navigation and control of robotic mechanisms.
Improving Prediction with Causal Probabilistic Variables

The application of feature engineering in classification problems has been commonly used as a means to increase the classification algorithms performance. There are already many methods for constructing features, based on the combination of attributes but, to the best of our knowledge, none of these methods takes into account a particular characteristic found in many problems: causality. In many observational data sets, causal relationships can be found between the variables, meaning that it is possible to extract those relations from the data and use them to create new features. The main goal of this paper is to propose a framework for the creation of new supposed causal probabilistic features, that encode the inferred causal relationships between the target and the other variables. In this case, an improvement in the performance was achieved when applied to the Random Forest algorithm.
Discrimination of entangled photon pair from classical photons by de Broglie wavelength

Abstract Quantum optics largely relies on the fundamental concept that the diffraction and interference patterns of a multi-partite state are determined by its de Broglie wavelength. In this paper we show that this is still true for a mixed state with one sub-system being in a classical coherent state and one being in entangled state. We demonstrate the quantum-classical light discrimination using de Broglie wavelength for the states with all classical parameters being the same.
Demolish falsy ratings in recommendation systems

Many recommendation systems suffer from overrun false rating injections, which affect and shift generated recommendations in collaborative networks. Previous research focuses more on how to distinguish between profiles ignoring individual characteristics. This paper studies different behaviors and attributes of attackers and isolates them from genuine users. This paper suggested a new algorithm to filter two types of attacks, push and nuke attacks, where it evaluates users' ratings according to the proposed algorithm and classify the rating as melt, freeze, or normal according to the calculate variance from the center. The suggested algorithm gave high potentials and was able to filter efficiently such malicious attacks.
Deep Joint Discriminative Feature Learning and Class-Aware Domain Alignment for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to utilize the knowledge learned from source domain with labels to make predictions for target domain without labels. Most of existing methods are committed to aligning the global distribution information to narrow down the domain discrepancy between source and target domain, which show a promising achievement. However, these domain alignment methods can not completely eliminate domain shift because of the loose manifold structure, so that the adjacent samples located in a high-density data region from different classes are easily misclassified by the hyperplane learned from source domain. Another problem is that these methods only align the distributions in the domain level, while neglecting class-level distribution information, which may cause class misalignment among domains. In this paper, we propose to learn discriminative feature and align domain in class level which can learn better source discriminative feature representations and benefit domain alignment in the class level. The discriminative feature learning strategy is able to obtain a tight manifold structure and then promote a low-density separation between classes. Then, to perform fine-grained domain alignment and learn more transferable features, a class-aware domain alignment approach is proposed to align samples from the same class in source domain and target domain. Experiments on two datasets show that our proposal can improve the performance of deep unsupervised domain adaptation methods.
Physics learning media based Augmented Reality (AR) for electricity concepts

Abstract Physics concepts are unique because some of them are the abstract concepts so they need to be visualized so as easier to be understood. However, it is rarely the learning media that can combine the real and virtual visualization of abstract physics concepts in the same time. This present study aims to create the application that is used as the physics learning media to aid the virtual visualization of the abstract concept of physics combined with real visualization using augmented reality. The abstract concepts of physics that are visualized are electricity concepts. The waterfall method is the method utilized to test the application on the android system. There are four standards to analyze data, namely functional suitability, performance efficiency, portability, and usability. The findings reveal that two standards (i.e., performance efficiency, and portability) lead the application is valid and good to be used while the usability standard achieves high value based students’ questionnaire that means the application is valid. The implication of this present study indeed can be widened to implement the application on the physics learning in the classroom.
Students' Intentions on Cyber Ethics Issues

Cyber ethical decisions have grave moral, legal, and social consequences on individuals, organizations, and societies at large. This chapter examines the extent of cyber unethical intentions among students on cyber piracy, cyber plagiarism, computer crime and abuses, and cyber privacy infringement. Using frequency analysis and the t-test of independent samples, the results showed that almost 24% of the respondents have intentions to engage in cyber piracy and about 13% would infringe on others privacy in cyberspace. More respondents have intentions to commit cyber piracy as compared to other cyber ethic issues, while cyber privacy infringement was the least observed. Almost 30% of respondents had intentions to commit software piracy, and 18.6% would engage in hacking activities. Also, cybercrime and computer abuse were more common among males than females. Cyber plagiarism was significantly higher among foreign students when compared to local students. Cyber piracy, cyber plagiarism, computer crime, and cyber privacy infringement were significantly higher in public universities.
Achieve Web Search Privacy by Obfuscation

After personal information of up to 87 million users of Facebook may be leaked to the consulting firm Cambridge Analysis in March 2018, some hackers sold 870 million user information stolen from 38 popular websites on the Darkweb in February 2019. The issue of privacy protection has once again received intense attention from people. One significant privacy risk that we encounter every day is searching across various search engines. The search engines can be curious and save users’ query histories. Then the search company and adversary can dig out the users’ intentions and points of interest by some technology such as machine learning. They may deliver advertisements to the user accurately. Even some malicious attackers will threaten users’ personal and property safety. A popular tool named TrackMeNot can help to protect users’ search privacy to a certain extent. TrackMeNot can maintain a dynamic list of popular queries and send dummy queries to obscure users’ real intentions. It is said they can significantly increase the difficulty for 3rd-parties to filter out users’ real queries. The work by [2] tries to find the deficiency of TrackMeNot. They use clustering or classifying method to filter out the real queries from the queries. In this paper, we make two useful improvements to TrackMeNot. By improving the generation strategy of dummy queries, we can effectively reduce the filtering effect of clustering methods and common classifiers on dummy queries. Experiments with the real query log dataset AOL show the effectiveness of our work.
Encryption of Association Rules Using Modified Dynamic Mapping and Modified (AES) Algorithm

Data mining which also sometimes called (knowledge discovery) or (data discovery) is the method of analysing hidden patterns of data according to different viewpoints of categorisation (clustering, classification, association rules, sequence mining) and find meaningful information. Retaining the security of the data when we need mining can be in a different technique according to data type, security need, and performance. This paper is focused on case there is need to obtain all the rules with highly sensitive data and for that reason using technique of regular Privacy-Preserving Data Mining (PPDM) will cause to hide some sensitive rules that intensely need it, proposed solution is by using encryption algorithm to secure the rules that get from mining algorithm.
Accelerating winograd convolutions using symbolic computation and meta-programming

Convolution operations are essential constituents of convolutional neural networks. Their efficient and performance-portable implementation demands tremendous programming effort and fine-tuning. Winograd's minimal filtering algorithm is a well-known method to reduce the computational complexity of convolution operations. Unfortunately, existing implementations of this algorithm are either vendor-specific or hard-coded to support a small subset of convolutions, thus limiting their versatility and performance portability. In this paper, we propose a novel method to optimize Winograd convolutions based on symbolic computation. Taking advantage meta-programming and auto-tuning, we further introduce a system to automate the generation of efficient and portable Winograd convolution code for various GPUs. We show that our optimization technique can effectively exploit repetitive patterns, enabling us to reduce the number of arithmetic operations by up to 62% without compromising numerical stability. Moreover, we demonstrate in experiments that we can generate efficient kernels with runtimes close to deep-learning libraries, requiring only a minimum of programming effort, which confirms the performance portability of our approach.
Extracting Biographical Spatial Timelines: Corpus and Experiments

This article presents a corpus and experiments to extract spatial timelines from biographies. Spatial timelines capture where someone is and is not located, and specify when this spatial information is true. We work with 100 Wikipedia biographies, and consider intersentential (location, year) pairs as well as years that are not explicitly stated in the biographies. Experimental results show that a combination of LSTMs outperforms SVM with linguistically motivated features.
Hybrid Method as Pseudo-Random Bits Generator

This paper aims to combine among Exponential chaotic map, Linear Congruential Generators (LCG), Multiplicative Congruential Generators (MCG) and RIPEMD-160 to create new pseudo arbitrary number generator. The guideline of the proposed technique involves generating key sequences by Exponential chaotic map to be utilised as coefficients in Linear Congruential Generators and Multiplicative Congruential Generators that generate arbitrary numbers. Those randomised numbers are converted to binary numbers and be used as inputs in RIPEMD-160 that produce a stream of hexadecimal numbers which are transformed into a sequence of binary digits. These binary digits are arranged as 16 × 10 array and permuted using the shifting process for each row randomly. The new pseudo-random bit generator was successfully tested in the NIST statistical test. The statistical and security analysis indicates it has large key spaces, very sensitive initial conditions and resistance to brute force and differential attack.