Reviewerly/exHarmony huggingface dataset:
AI as an Inventor: Has the Australian Federal Court Erred in DABUS?

The emergence of advanced Artificial Intelligence (AI) technologies has caused an international debate as to whether inventions generated by AI technology without human intervention should be protected under patent law and who should own them. These questions have been discussed in a recent Federal Court of Australia decision in Thaler v Commissioner of Patents. In that judgment, Beach J recognised that some AI has the ability to autonomously invent and that such AI-generated inventions could be protected under patent law. His Honour held that, in such instances, an AI system could and should be listed as an inventor in a patent application. This article challenges the decision by arguing that, even in the case of the most sophisticated AI systems, these systems are not autonomous in the inventive process as humans provide significant contributions to the very system that leads to the inventive output. Secondly, I contend that the discussion on the need of patent protection for AI-generated inventions (if it were possible at all) is misplaced and not sufficiently comprehensive. Finally, the expanded application of the Patents Act 1990 (Cth), and especially s 15(1), to accommodate ‘AI inventors’, is an over-reach that is not consistent with the current law.
Superposition

One of the defining characteristics of quantum mechanics is the possibility of a superposition of states.
Cloud Computing in E-Governance

Cloud Computing is becoming a rapidly accepted and deployed paradigm both by individuals and organizations alike. The government of various countries is also moving its services to cloud to offer better and just in time services to the users. This chapter explores the basic concepts of Cloud Computing, which includes the main features of Cloud Computing, the cloud deployment models, the services offered by the cloud, motivations behind adoption of cloud by organizations, in general and by the Government, in particular. We also lay an insight into the various Cloud Computing initiatives taken by the Government of India to facilitate its citizens with easy access to information/services.
Combination of First Order Statistical Feature and Gray Level co-occurence Matrix for Cirebon Mask Classification

Indonesia culture captivates people both domes-tically and internationally. One of the prominent cultures is Cirebon Mask. There are five types of Cirebon masks: Panji, Samba, Rumyang, Tumenggung, and Kelana. Sadly, the Cirebon mask is already on the verge of extinction because of the local people do not want to learn and preserve the understanding of Cirebon mask. To prevent extinction, we digitise the mask and build a machine learning model to categorized the Cirebon Mask based on its type, which will be used as content-based image retrieval. In this work, we present texture features using a combination of first-order statistical features and GLCM features while K-Nearest Neighbour and Support Vector Machine acted as the classifier. The best accuracy of this research is 92.96 %
Security in Databases using Cloud Computing

The initiation of the Internet of Things is the fundamental stimulus behind the current mechanical surprise. Web of things is the unavoidable aggregation of web related devices that assemble, look at and change the immense measure of colossal data at an incomparable rate. By creating and passing on sensible preprocessing frameworks, this goliath measurement of data can be transformed into performance information. The all-new IoT tremendous information development expects changes to be passed on to the present advances. The significance of preprocessing methodologies in the IoT enormous information situation has been discussed in this document and in addition to early IoT examines huge information preprocessing frameworks. Finally, a bright fresh parallel preprocessing system for IoT Big data has been suggested to transform the tough data into treasurable information so that enormous data examination of IoT performance can obtain complete recognition of this increasing growth
A Quantum Secure Entity Authentication Protocol Design for Network Security

Authentication is one of the significant issues for all kinds of network communications. Most of the authentication protocols designed and implemented so far for entity authentication are based on classical cryptographic techniques to prevent themselves from different types of attacks. These protocols use either password or challenge for authentication. In this article, the design of the proposed quantum secure entity authentication protocol is shown. The proposed protocol is based on the challenge response method. Due to quantum computer capability to break mathematical complexity-based cryptographic techniques, the proposed protocol uses the one-time pad (OTP) to secure itself from attacks, i.e., eavesdropping, reply attack, password guessing attack, man-in-the-middle attack, brute-force attack, quantum computer attack, etc. Security of the proposed protocol was analyzed, and it shows that the proposed protocol may prevent itself from different types of attacks. Further, analysis for quantum Secure was carried out. From the analysis, it is found that if the OTP key is truly random and cannot be reused, then a computer with infinite capacity or quantum computer cannot break the encrypted challenge and response. The proposed protocol may be used for entity authentication for the client, server, process, and user.
Cloud-based Parallel Computing System Via Single-Client Multi-Hash Single-Server Multi-Thread

Parallel Distributed Processing is a relatively new method. Distributed cloud connects data and applications delivered by utilizing cloud computing technologies from several geographical locations. When something is distributed in IT, it is transferred among multiple systems located across diverse areas. The expanse of information besides the interval it takes towards analyzing adding to monitoring projected results efficiently and effectively has been dramatically enhanced. This paper presented a system to assist users in doing composite tasks interactively with the least processing time. Distributed-Parallel-Processing (DPP) and CloudComputing (CC) are the two most great technologies, can process and answer the user problem quickly. Developing a suggested system used several sources (source generators, under-test load, computing machines, and processing units) and (webservers) through the cloud. Hash codes are generated on the client-side and sent towards the webserver. The webserver delivers these does to cracking servers that have been specified. It has been verified that while employing light load (single hash-code) with multi-servers and multiprocessors, the suggested system gives improved efficiency (in terms of Kernel-burst, User-bust, and Total-execution) timings. Although it has been demonstrated that employing large loads (many under-testing codes) with many computing machines using multiprocessors improves the system’s performance. The suggested computational system outperforms it in terms of parallel processing. The proposed method took these situations into account due to the code-breaking influences by two precarious criteria (minimal breaking-time besides cost-effective usage of computer resources).
Technological Innovation in Academic Libraries Among Universities

Innovation is no longer an option but a necessity for an organization to survive during a crisis. Innovations in terms of products, process, technologies, and services, can effectively be used to resolve the crisis of the current educational system to survive and thrive in the 21st century. Academic libraries should re-think and re-invent the existing technologies, services, and facilities to fulfill the demands of users. Management, organization, and dissemination of information can be done quickly and effectively with the application of information and communication technology (ICT) in an innovative way. Technological innovation (TI) can be considered as an innovative solution for the sustenance of libraries during a crisis. This chapter attempts to describe the essence of TI in academic libraries and highlights the perceptions of librarians on TI in the university libraries system in India. Therefore, this chapter will explore individual innovative behavior and its influencing factors on technological innovation in academic libraries in Indian universities.
A cross-modal multimedia retrieval method based on semi-supervised learning and category information alignment

With the rapid development of artificial intelligence technology, traditional unimodal media information retrieval can hardly meet users’ retrieval needs for different data, therefore, cross-modal information retrieval is the key to breakthrough and solve the problem, however, the difficult problem faced by cross-modal information retrieval is how to effectively and accurately semantically match heterogeneous data and compare media objects of different modalities in different feature spaces. To address the difficult problem of cross-modal information retrieval, this paper proposes a cross-modal multimedia retrieval method with semi-supervised learning and category information alignment, which aligns different modal information by introducing category information to minimize the discriminative loss in the common representation space and the loss among individual modalities, and then constructs a homogeneous high-level semantic space based on the underlying feature space of different modal objects, and applies the semi-supervised learning method to cross-media retrieval. The semi-supervised learning method is applied to cross-media retrieval to achieve semantic matching of multimedia objects of different modalities. Finally, by comparing this method with traditional cross-media retrieval methods, the experimental results show that this method has greatly improved the accuracy of retrieval results compared with traditional cross-media retrieval techniques.
A Generalized Parallel Quantum Inspired Evolutionary Algorithm Framework for Hard Subset Selection Problems

Quantum-inspired evolutionary algorithms (QIEAs) like all evolutionary algorithms (EAs) perform well on many problems but cannot perform equally better than random for all problems due to the No Free Lunch theorem. However, a framework providing near-optimal solutions on reasonably hard instances of a large variety of problems is feasible. It has an effective general strategy for easy incorporation of domain information along with effective control on the randomness in the search process to balance the exploration and exploitation. Moreover, its effective parallel implementation is desired in the current age. Such a Generalized Parallel QIEA framework designed for the solution of Subset Selection Problems is presented here. The computational performance results demonstrate its effectiveness in the solution of different large-sized hard SSPs like the Difficult Knapsack Problem, the Quadratic Knapsack Problem and the Multiple Knapsack problem. This is the first such a generalized framework and is a major step towards creating an adaptive search framework for combinatorial optimization problems.
Markov Random Fields

This chapter presents an introduction to Markov random fields (MRFs), also known as Markov networks, which are undirected graphical models. We describe how a Markov random field is represented, including its structure and parameters, with emphasis on regular MRFs. Then, a general stochastic simulation algorithm to find the optimum configuration of a MRF is described, including some of its main variants. The problem of parameter estimation for a MRF is addressed, considering the maximum likelihood estimator. Conditional random fields are also introduced. The chapter concludes with two applications of MRFs for image analysis, one for image de-noising, and the other for improving image annotation by including spatial relations.
Pattern Recognition of Handwritten English Characters

After success of a total solution to handwriting 99 multiplication by deep learning, this chapter further addresses on the problem with increased complexity. In addition to handwritten digital dataset, the EMNIST database provides multiple balanced or unbalanced datasets. These datasets contain different combinations of handwritten digit and letter images. It is believed that well trained deep CNNs can handle unbalanced datasets, so this chapter chose By_Class of EMNIST database as a dataset to increase the difficulty of problem solving and extend the application of iOS Apps. This chapter discusses classification of handwritten English character, including uppercase and lowercase, data audition due to requirement of further improvement, and online tests on iOS devices. After a long time of training, the developer got the pre-trained CNN model. For 58,405 testing images, the recognition accuracy rate was as high as 97.0%.
A Study on the Activity Types in Church's Shift into Local Ministry and Local Community Education

A Study on the Activity Types in Church's Shift into Local Ministry and Local Community Education - local ministry;church's public responsibility;mutual respect;communication;intimacy;spirituality;Missio Dei
Steganography Using Pixel Value Differencing and Diamond Encoding Based on Android

The PVD method in steganography used to embed secret message to image file by modifying pixel pair value.The addition of Diamond Encoding is to make difference of pixel pair dynamic so the difference in image's pixel can't be seen with naked eye.To make easier for user to choose and share image file, this application will be developed on Android smartphone.
The analysis of evolutionary optimisation on the TSP(1,2) problem

TSP(1,2) problem is a special case of the travelling salesperson problem which is NP-hard. Many heuristics including evolutionary algorithms (EAs) are proposed to solve the TSP(1,2) problem. However, we know little about the performance of the EAs for the TSP(1,2) problem. This paper presents an approximation analysis of the (1+1) EA on this problem. It is shown that both the (1+1) EA and (µ + λ) EA can obtain 3/2 approximation ratio for this problem in expected polynomial runtime O(n3) and O ((µ/λ)n3 + n) , respectively. Furthermore, we prove that the (1+1) EA can provide a much tighter upper bound than a simple ACO on the TSP(1,2) problem.
Machine Learning Application in LAPIS Agile Software Development Process

It is necessary to carry out improvement works at every stage of the software processes to adapt to changes in the world of software. For this reason, primarily, the processes should be specific, automatable, and measurable; the opinions of the teams on improvement should be supported both for these processes and their own works. In this study, the contribution of the teams to the improvement works in the retrospective meeting, which is part of the improvement-oriented agile product development process, LAPIS (Logo Agile Process Improvement System) developed by LOGO, by being inspired by the lean manufacturing concept, has been supported by machine learning. The aim has been to expand the opportunities for the works of teams to improve and to be based on data. The effects of the study results on the software process measurement results have been examined, and the effects of the improvements on the company noticed by the teams have been evaluated.
Safeguarding of ATM

An automated teller machine (ATM) is a kiosk that is used widely for money transactions across the globe. Several banking sectors have showed interest in deploying ATMs. The cash dispenser system manages the transaction services with less manual effort. When it comes to deploying an ATM, two methods are practiced: onsite ATM and offsite ATM. Safeguarding cash kept inside the ATM is a challenge. Researchers suggested several built-in security measures to secure the money in ATMs. Nevertheless, burglars still loot the money. Some widely used looting methods include card skimming, cash trapping, and phishing. So, it is time to give intelligence to the ATM itself to react to the situation. Proposed is a system that implements the idea of making machines to identify the situation and perform actions accordingly. This mechanism is not only about giving intelligence to it, but also a cost-effective one.
An Era of Face Filters

Smartphones are widely growing among today’s generation and the main reason for it is the social media which helps connect people whenever and wherever they are in any part of this world. Social media apps like Snapchat got it’s popularity because of it’s unique features like face filters. Face filters are widely used among teenagers for fun and to look different.It has the ability to entertain by applying different types of filters on a person’s face by recognizing it’s facial features and make it look fun built caught popularity very fast and is growing every day.The augmented reality technology works flawlessly behind the filters.The technology first originated in Russia technical company which afterwards was overtaken by“snapchat” and grew constantly.The aim of this article is to create an application that can recognise a human face and its features and accordingly apply face filters. Using this feature user can view the content with great experience.
Using the Diagnostic Odds Ratio to Select Patterns to Build an Interpretable Pattern-Based Classifier in a Clinical Domain: Multivariate Sequential Pattern Mining Study

It is important to exploit all available data on patients in settings such as intensive care burn units (ICBUs), where several variables are recorded over time. It is possible to take advantage of the multivariate patterns that model the evolution of patients to predict their survival. However, pattern discovery algorithms generate a large number of patterns, of which only some are relevant for classification.We propose to use the diagnostic odds ratio (DOR) to select multivariate sequential patterns used in the classification in a clinical domain, rather than employing frequency properties.We used data obtained from the ICBU at the University Hospital of Getafe, where 6 temporal variables for 465 patients were registered every day during 5 days, and to model the evolution of these clinical variables, we used multivariate sequential patterns by applying 2 different discretization methods for the continuous attributes. We compared 4 ways in which to employ the DOR for pattern selection: (1) we used it as a threshold to select patterns with a minimum DOR; (2) we selected patterns whose differential DORs are higher than a threshold with regard to their extensions; (3) we selected patterns whose DOR CIs do not overlap; and (4) we proposed the combination of threshold and nonoverlapping CIs to select the most discriminative patterns. As a baseline, we compared our proposals with Jumping Emerging Patterns, one of the most frequently used techniques for pattern selection that utilizes frequency properties.We have compared the number and length of the patterns eventually selected, classification performance, and pattern and model interpretability. We show that discretization has a great impact on the accuracy of the classification model, but that a trade-off must be found between classification accuracy and the physicians' capacity to interpret the patterns obtained. We have also identified that the experiments combining threshold and nonoverlapping CIs (Option 4) obtained the fewest number of patterns but also with the smallest size, thus implying the loss of an acceptable accuracy with regard to clinician interpretation. The best classification model according to the trade-off is a JRIP classifier with only 5 patterns (20 items) that was built using unsupervised correlation preserving discretization and differential DOR in a beam search for the best pattern. It achieves a specificity of 56.32% and an area under the receiver operating characteristic curve of 0.767.A method for the classification of patients' survival can benefit from the use of sequential patterns, as these patterns consider knowledge about the temporal evolution of the variables in the case of ICBU. We have proved that the DOR can be used in several ways, and that it is a suitable measure to select discriminative and interpretable quality patterns.
Lawful Trojan Horse

News outlets don't usually report on training methods in counter-cyberterrorism, particularly lawful trojan attacks. Instead they describe recent cyberterrorist attacks, or threats, or laws and regulations concerning internet privacy or identity theft. Yet Europe is looking to do just that to head-off the next major cyberattack by creating rules for how member states should react and respond. Several news outlets, for example, reported that Germany's Federal Criminal Police Office (BKA) were using a Trojan Horse to access the smartphone data of suspected individuals before the information was encrypted. Although the urge to strike back may be palpable, hacking-back can put power back into the hands of the suspect. The consensus now is that government action is preferable to hacking-back at attackers.
Seismicity of Antarctica: Features

In this paper, the spectral characteristics of seismic data obtained at various seismic stations in Antarctica are studied using the spectral histogram method developed by the authors and the study of regional structures.This takes into account the fundamental features of the geological, geophysical and astrophysical picture of the entire continent; significant component of fragile crustal structures.The possibility of the existence in the polar region of the ancient structures of the plume during its formation experienced the impact of centrifugal forces from the rotation of the Earth and the inhibition of the top of the plume in the low-temperature near-surface layer.One of the most significant attractions of the region is the existence of a large-sized ozone "hole".All the above features have found their reflection in the seismic fields of Antarctica.
HDR Image Construction from Trifocal Multiexposure Images

With the progress of autonomous vehicles, the sensing of the environment in more detail with higher dynamic ranges has become more important to classify surrounding objects and obstacles. While stereo HDR images for this purpose can provide advantages compared to the conventional LDR images, they suffer from limited dynamic ranges and spike-like noises due to the inaccuracies in disparity estimation. In this paper, we formulate the HDR image construction problem from trifocal multi-exposure images and develop a method which improves disparity estimation for better HDR image construction. Given the symmetric geometry of the trifocal setup, the proposed method uses the equivalence of disparities from middle to left and middle to right images to determine the reliable regions. The HDR radiance for the pixels in these reliable regions are estimated by using the weighted average of the warped images in different exposures and the middle image, whereas the radiance values outside the reliable regions are estimated by using only the middle image. The experiments with different exposure combinations for left, middle and right images reveal better performances of the proposed method compared to the stereo HDR imaging. It is also observed that the improvements are more apparent for larger disparities between the cameras.