Reviewerly/exHarmony huggingface dataset:
Logic and Learning (Dagstuhl Seminar 19361).

The goal of building truly intelligent systems has forever been a central problem in computer science. While logic-based approaches of yore have had their successes and failures, the era of machine learning, specifically deep learning is also coming upon significant challenges. There is a growing consensus that the inductive reasoning and complex, high-dimensional pattern recognition capabilities of deep learning models need to be combined with symbolic (even programmatic), deductive capabilities traditionally developed in the logic and automated reasoning communities in order to achieve the next step towards building intelligent systems, including making progress at the frontier of hard problems such as explainable AI. However, these communities tend to be quite separate and interact only minimally, often at odds with each other upon the subject of the ``correct approach'' to AI. This report documents the efforts of Dagstuhl Seminar 19361 on ``Logic and Learning'' to bring these communities together in order to: (i) bridge the research efforts between them and foster an exchange of ideas in order to create unified formalisms and approaches that bear the advantages of both research methodologies; (ii) review and analyse the progress made across both communities; (iii) understand the subtleties and difficulties involved in solving hard problems using both perspectives; (iv) make attempts towards a consensus on what the hard problems are and what the elements of good solutions to these problems would be.The three focal points of the seminar were the strands of ``Logic for Machine Learning'', ``Machine Learning for Logic'', and ``Logic vs. Machine Learning''. The seminar format consisted of long and short talks, as well as breakout sessions. We summarise the motivations and proceedings of the seminar, and report on the abstracts of the talks and the results of the breakout sessions.
Variational quantum control for single- and two-qubit transmon gates

APS March Meeting. American Physical Society, Monday–Friday, March 2–6, 2020; Denver, Colorado (USA).--.https://meetings.aps.org/Meeting/MAR20/SearchAbstract7
LibGuides: NEW Full Text Resources Available During Library Closure: Home

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LibGuides: Free E-Resources During the COVID-19 Pandemic: Video conferencing

Many providers are temporarily making their services or content available for free in order to support distance learning.
Translators4u Android Application

Language can be a barrier in a communication when the message conveyed to the other person is not reached. This problem is commonly faced when travelling to other countries especially at places that only speak their own native language. Taking into advantage the applications that can be downloaded into smartphones, a translation application is created. Translators4u is an instant translation application. It support with 81 languages to let the user choose the pair languages needs, then the system automatically appear the translators who speaks those languages to the user. Users can easily make voice call by SIM number or Skype with translators, also they are able to see translators profile and their location flag. The system serves users with reservation service to easily get translators on time. By looking at the different needs of people during their trip to different countries this application can serve those with translation problems.
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With the technology of the 4th Industrial Revolution integrated into the industry as a whole, digital transformation (DT) is becoming an innovative...
A Review on Cloud Computing Security: Issues and Solutions

The word Cloud came into focus from the graphic that was often used to show the heterogeneous networks and complex infrastructure. Cloud computing is the next generation networks which is soon going to revolutionize the computing world. Security in Cloud computing is an important issue and critical parameters, and has number of issues and problem related to it. Cloud service provider and the cloud service consumer should make sure that the cloud is safe enough from all the external threats so that the customer does not face any problem such as loss of data or data theft. There is also a chance where a misbehaviors user can affect the cloud by impersonating a legitimate user, thus creating problem to the entire cloud and affects many more customers who are sharing the affected cloud. This paper works on security issues in cloud computing as well as related security concerns suggested in literature.
Activation of Students in Homothetic Transformation

This article discusses ways to activate the cognitive activity of students in the study of the topic of transformation of orthogonal projections. Homothetic transformation is used as examples.
Analysis of the Relationship Between Psychological Manipulation Techniques and Both Personality Factors and Behavioral Characteristics in Targeted Email

To succeed in targeted email attacks, the attacker attempts to create a targeted email that is difficult for the target to distinguish from legitimate email. This is the reason why attackers use Open Source Intelligence (OSINT) to try to gather information about targets. Besides this, recent AI technologies make the matter worse now. We consider the possibility of a new type of attack where the attacker uses AI tools to estimate the personality and behavioral characteristics of the target based on the information obtained from OSINT tools, and create malicious emails with highly effective psychological manipulation for specific targets. In this paper, to confirm the danger of that targeted email attack, the relationship between a psychological manipulation technique (Cialdini’s principles) and both individual personality and behavioral characteristics was experimentally investigated. The analysis proved that Cialdini’s principles introduced in sentences of the targeted emails show different trends based on both personality factors and behavioral characteristics of the individual.
Deep Learning Methods for Image Matching and Camera Relocalization

Deep learning and convolutional neural networks have revolutionized computer vision and become a dominant tool in many applications, such as image classification, semantic segmentation, object recognition, and image retrieval. Their strength lies in the ability to learn an efficient representation of images that makes a subsequent learning task easier. This thesis presents deep learning approaches for a number of fundamental computer vision problems that are closely related to each other; image matching, image-based localization, ego-motion estimation, and scene understanding. In image matching, the thesis studies two methods utilizing a Siamese network architecture for learning both patch-level and image-level descriptors for measuring similarity using Euclidean distance. Next, it introduces a coarse-to-fine CNN-based approach for dense pixel correspondence estimation that can leverage the advantages of optical flow methods and extend them to the case of wide baseline between two images. The method demonstrates good generalization performance and it is applicable for image matching as well as for image alignment and relative camera pose estimation. One of the contributions of the thesis is a novel approach for recovering the absolute camera pose from ego-motion. In contrast to the existing CNN-based localization algorithms, the proposed method can be directly applied to scenes which are not available at training stage and it does not require scene-specific training of the network, thus, improving the scalability. The thesis also shows that Siamese architecture can be successfully utilized in the problem of relative camera pose estimation achieving better performance in challenging scenarios compared to traditional image descriptors. Lastly, the thesis demonstrates how the advances of visual geometry can help to efficiently learn depth, camera ego-motion, and optical flow for the task of scene understanding. More specifically, it introduces a method that can leverage temporally consistent geometric priors between frames of monocular video sequences and jointly estimate ego-motion and depth maps in a self-supervised manner.
Shuffled Frog-Leaping Programming for Solving Regression Problems

There are various automatic programming models inspired by evolutionary computation techniques. Due to the importance of devising an automatic mechanism to explore the complicated search space of mathematical problems where numerical methods fails, evolutionary computations are widely studied and applied to solve real world problems. One of the famous algorithm in optimization problem is shuffled frog leaping algorithm (SFLA) which is inspired by behaviour of frogs to find the highest quantity of available food by searching their environment both locally and globally. The results of SFLA prove that it is competitively effective to solve problems. In this paper, Shuffled Frog Leaping Programming (SFLP) inspired by SFLA is proposed as a novel type of automatic programming model to solve symbolic regression problems based on tree representation. Also, in SFLP, a new mechanism for improving constant numbers in the tree structure is proposed. In this way, different domains of mathematical problems can be addressed with the use of proposed method. To find out about the performance of generated solutions by SFLP, various experiments were conducted using a number of benchmark functions. The results were also compared with other evolutionary programming algorithms like BBP, GSP, GP and many variants of GP.
DESIGNING EDUCATIONAL KIT TO INVOLVE CHILDREN IN COLLABORATIVE LEARNING ACTIVITIES AT SCHOOL

The research started from a reflection on the collaborative learning, as an approach to teaching and learning involving groups of students working together according to specific goals.The work focused on the cultivation of green areas in urban locations, focusing particularly on the vegetable garden. Born as a need in the big cities during the war, and after its downfall during the economic boom, in Italy and all over the world, the vegetable garden makes a comeback according to the contemporary needs of environmental sustainability.Nowadays the vegetable garden cultivation is practiced even within urban buildings for domestic purpose and in schools courtyards as a collaborative learning strategy for children.According to this approach this work has focused on indoor learning environments at kindergarden and primary school. This work aims at helping this learning practise based on agricultural activity at school, through the use of a kit cointaining a series of products and an Application with AR technology for tablet that allows the cultivation according to a tutoring method and a memorandum of all the farming phases. The project, based on the renewed interest in biological products and their seasonality, proposes tools for children, able to detect the indoor climate conditions and, according to them, let the appropriate cultivations grow, thus avoiding the need for a controlled micro-climate, such as that of greenhouses or hydroponic systems.The final aim is not only to facilitate children to the cultivation, but also to find new didactic instruments to convey knowledge of natural science starting from specific manuals about vegetables and farming, completed by suggested recipes for the usage of the harvested fruits/vegetables. It is a whole productive cycle, starting from the knowing of the product, the cultivation and the plant, moving from theory to practice, following the farming step by step, up until the harvesting and consuming of the final product. This activity makes children able to t to take care of their plantation in a playful and active way and at the same time teaching them practical and theoretical activities with the final goal to take care of themselves of the nature and of biological ecosystems of our planets
Cryptographic Reverse Firewalls for Interactive Proof Systems.

Abstract We study interactive proof systems (IPSes) in a strong adversarial setting where the machines of honest parties might be corrupted and under control of the adversary. Our aim is to answer the following, seemingly paradoxical, questions: • Can Peggy convince Vic of the veracity of an NP statement, without leaking any information about the witness even in case Vic is malicious and Peggy does not trust her computer? • Can we avoid that Peggy fools Vic into accepting false statements, even if Peggy is malicious and Vic does not trust her computer? At EUROCRYPT 2015, Mironov and Stephens-Davidowitz introduced cryptographic reverse firewalls (RFs) as an attractive approach to tackling such questions. Intuitively, a RF for Peggy/Vic is an external party that sits between Peggy/Vic and the outside world and whose scope is to sanitize Peggy's/Vic's incoming and outgoing messages in the face of subversion of her/his computer, e.g. in order to destroy subliminal channels. In this paper, we put forward several natural security properties for RFs in the concrete setting of IPSes. As our main contribution, we construct efficient RFs for different IPSes derived from a large class of Sigma protocols that we call malleable. A nice feature of our design is that it is completely transparent, in the sense that our RFs can be directly applied to already deployed IPSes, without the need to re-implement them.
Modulated Self-attention Convolutional Network for VQA.

As new data-sets for real-world visual reasoning and compositional question answering are emerging, it might be needed to use the visual feature extraction as a end-to-end process during training. This small contribution aims to suggest new ideas to improve the visual processing of traditional convolutional network for visual question answering (VQA). In this paper, we propose to modulate by a linguistic input a CNN augmented with self-attention. We show encouraging relative improvements for future research in this direction.
McDonald's Japan: AR and IoT Marketing Strategy with Pokemon GO

This business case introduces the marketing campaign launched by McDonald’s Japan in July 2016, using the new smartphone version of the popular game Pokemon GO, whose extraordinary success attracted the attention of social platforms, businesses, and academic audiences. The alliance between McDonald’s Japan and Niantic-Pokemon is a good example of the social impact of modern games based on geolocation and augmented reality, and how this can embody new business models and marketing strategies. For McDonald’s Japan, this alliance attracted a large number of new consumers to its restaurants at a trivial cost. The results for the company were immediate and significant, driven by the community of 3.4 million game subscribers in Japan: it received 1.5 to 2 million daily visits across its 3,000 locations, had an estimated sales increase of 22%, and earned an increase in market capitalization of 9.8%. This marketing strategy connected the underlying culture of the former players of the first Pokemon GO for Nintendo DS with the values of the McDonald’s brand, attracting to the restaurants a new segment of customers aged from their mid-30s to their 40s as well as their children.
End to End Learning in Autonomous Driving Systems

Author(s): Gao, Yang | Advisor(s): Darrell, Trevor | Abstract: Convolutional neural networks have advanced visual perception significantly in recent years. Two major ingredients that enable such a success are the composition of simple modules into a complex network and the end to end optimization. However, such success has not yet revolutionized robotics as much as vision, even if robotics suffer from similar problems as traditional computer vision, i.e. imperfectness of the manual pipeline design of the system. This thesis investigates using end-to-end learning for the autonomous driving system, a concrete robotic application. End to end learning can produce reasonable driving behaviors, even in the complex urban driving scenarios. Representation learning in end-to-end driving models is crucial, and auxiliary vision tasks such as semantic segmentation can help to form a more informative driving representation especially when training data is limited. Naive convolutional neural networks are usually only capable of doing reactive control and can not involve complex reasoning in a particular scenario. This thesis also studies how to handle scene conditioned driving behavior, which goes beyond the capability of reactive control. Alongside the end-to-end structure, learning methods also play a critical role. Imitation learning methods will acquire meaningful behaviors but usually, the robot can not master the skill. Reinforcement learning, on the contrary, either barely learns anything if the environment is too complex, or it can master the skill otherwise. To get the best of both worlds, this thesis proposes an algorithmically unified method to learn from both demonstration data and the environment.
Study on the Secondary Pollution of Building Fire Water Storage and Domestic Water

Abstract In order to solve the maintenance and management problems of the fire water tank in the fire water supply system, the problem of how to prevent the secondary pollution of domestic water by using the water source for fire storage water and domestic water is discussed. The countermeasures and the concept of intrinsic safety using shared fire-fighting water tanks are proposed, and it is clear that fire-fighting pools (boxes) need to strengthen water quality and the intrinsic safety of the pools. The innovative construction of the fire water tank system architecture is conducive to improving the reliability of fire water supply for buildings.
Active Reading for Intercomprehension between Sinogramic Languages

Abstract In the domain of e-learning for a linguistic field, active reading interfaces are useful tools for learning new languages. For a learner of a given language, the goal is to assist in knowledge development in other known languages, in order to enhance their learning. An active reading program enriches the understanding of the input sentence in different ways, such as adding the pronunciation of each word or displaying a definition or a translation taken from a dictionary. By the process of analogy, the users would then be able to synthesize the information and produce themselves a translation that would be, at their convenience, both syntactically and semantically appropriate. We propose two new features for an active reading interface that aims to help in the understanding of Sinogramic languages. We focus on Japanese and Mandarin languages. The first feature identifies the reading (on’yomi, kun’yomi…) of the sinograms; the second feature displays, for certain words of the input text, their equivalent in another language that is etymologically close to the source one. In our case, the words with on’yomi reading will be associated with their Mandarin equivalents, in their Pïnyïn form. This tool can be useful for learners of Japanese with some knowledge of Mandarin or the contrary.
MODERN DIGITAL ASSESSMENT SYSTEMS &amp; TOOLS: THE MEASUREMENT OF STUDENT ABILITY &amp; HUMAN FACTOR EXCLUSION

Digital technologies simultaneously demand, reflect, and build students’ skills in many old and new areas of learning. They are capable to improve students' learning process as well as students' achievements. New methods need to be developed to use this capacity in a manner that can produce useful and accurate information to teachers for classroom interventions and education system. The second very important element is Human Factor Exclusion. Issues related to Human Factors can arise in any situation and judgment-based analytical process.The outcomes-based on Measurement of Student Ability is very important in national level because they can provide the proof and the evidence-based arguments that the educational system is seeking its aim regarding the assumed learning objectives to the stakeholders (employers, trustees, public officials, accreditation, agencies, students, future candidates, sponsoring companies).During the last ten years, the Latvian education system has used various digital tools for assessing students’ achievements. Digital assessment decisions are used both in diagnostic tests for various age groups and national exams. As schools have the possibility to choose the format of diagnostic tests either on-line or traditional way on paper, it is possible to analyse the differences of the evaluation among them. How significant is the human factor and what impact does it have? What can be the errors of digital systems? What problems are students facing? What mistakes and shortcomings the instruments and design have (e.g. defective items, unreliable answer keys, response patterns)?While most of the studies about these methods are based on data from standardized tests, too less attention is paid to alternative analysis in the real school’s environment. The article analyses and generalizes the difference between the evaluation of students’ achievements by a teacher and by the digital system. Is the digital assessment system changing students’ behaviour during the test? In this paper, we report the results of a four-year-long experiment in diagnostic tests of 6th-grade students in Latvian education system.The most important aim of this article is to compare the difference between an assessment of students’ achievements using digital systems and evaluations done by teachers in a traditional way.The second aim is to analyse the impact of the assessment system for the behaviour of students during the test and how the digital system assists in evaluating students’ abilities.
An Efficient Cryptanalysis Scheme for Secure Data Storage System in Cloud Using optimal Dual Encryption Algorithm

Cloud computing gives adaptable infrastructure and high storage capacity for Big Data applications. Many of the cloud clients have been migrating or building their business into the cloud. However, various potential clients are as yet reluctant to take advantage of the cloud due to security and privacy concerns. Mainly, the proposed system consists of three modules such as: i) cryptographic security system (CSS) using dual encryption algorithm; ii) security blocking system using optimal double attack; and iii) generating optimal security system. At first, we design the security system using hybridisation of blowfish and data encryption standard (DES). From this, we evaluate how much amount of original information retrieved. Then, finally, to increase the security system, we design an optimal dual encryption based security system. At last, the quality of the work compared to other encryption techniques with minimum encryption time.