Reviewerly/exHarmony huggingface dataset:
Enhancement IDEA Algorithm with Digital Image as Key Encryption and Decryption

Abstract Cryptography is one type of protection that is widely used to secure information, encryption, and decryption and the use of keys is a common process used to secure information; and also the cryptographic processes have a classic problem that is the distribution of keys that are vulnerable to interception when the sender sends the key to the receiver, the cryptographic protocol (Authentication) could be used to minimize key distribution problem because there is no need for key exchanges. Shamir’s Three-Pass Protocol, Secret Splitting, Bit-Commitment Protocol and Blind Signature are few cryptographic protocols that can be used to help with key distribution issues, this article performs systematic approach to protocol cryptography for security level and the using other algorithms to combine with protocol.
Context-Aware Reasoning Framework for Multi-user Recommendations in Smart Home

This paper introduces a context-aware reasoning framework that adapts to the needs and preferences of inhabitants continuously to provide contextually relevant recommendations to the group of users in a smart home environment. User’s activity and mobility plays a crucial role in defining various contexts in and around the home. The observation data acquired from disparate sensors, called user’s context, is interpreted semantically to implicitly disambiguate the users that are being recommended to. The recommendations are provided based on the relationship that exist among multiple users and the decision is made as per the preference or priority. The proposed approach makes extensive use of multimedia ontology in the life cycle of situation recognition to explicitly model and represent user’s context in smart home. Further, dynamic reasoning is exploited to facilitate context-aware situation tracking and intelligently recommending appropriate actions which suit the situation. We illustrate use of the proposed framework for Smart Home use-case.
Considering Sub-graph Structure in Representation Learning

Abstract Representation learning aims to encode knowledge graphs into a low-dimensional vector space. However, since both the out-degree and in-degree of the entities in the knowledge graph follow the power-law distribution, only a small number of entities with higher frequency play a key role in training process, while the others have less effect. This leads to a more serious problem of data sparsity. In this paper, we propose two knowledge graph representation learning models for sub-graph structure fusion. Based on the original model, our models are trained by incorporating the sub-graph structure during training process. By incorporating the sub-graph information into the representation learning model, the structural associations between different relations can be incorporated while considering the semantic association between entities. So that the entities and relations can be modeled more accurately, and the data sparsity problem of the knowledge graph representation learning model can be alleviated effectively.
Foreign Languages Learning

This chapter examines which capabilities of interactive TV can be used to enhance the foreign languages learning experience in children and adolescents. In modern English language courses, interactive whiteboard software and interactive e-books provide several multimedia capabilities, whose relative transition in their iTV version can be of great benefit for students. The possible changes that the educational process may undergo due to the use of interactive TV in the light of the blended learning model, as well as the expected benefits of this transition are discussed in this chapter. It also examines how beneficial the use of iTV functions as a support process in the traditional classroom model would be and if it is feasible to use it autonomously entirely for the learning process, considering the specificities of the age group under consideration.
Speeding up Reinforcement Learning by Combining Attention and Agency Features.

When playing video-games we immediately detect which entity we control and we center the attention towards it to focus the learning and reduce its dimensionality. Reinforcement Learning (RL) has been able to deal with big state spaces, including states derived from pixel images in Atari games, but the learning is slow, depends on the brute force mapping from the global state to the action values (Q-function), thus its performance is severely affected by the dimensionality of the state and cannot be transferred to other games or other parts of the same game. We propose different transformations of the input state that combine attention and agency detection mechanisms which both have been addressed separately in RL but not together to our knowledge. We propose and benchmark different architectures including both global and local agency centered versions of the state and also including summaries of the surroundings. Results suggest that even a redundant global-local state network can learn faster than the global alone. Summarized versions of the state look promising to achieve input-size independence learning.
All-glass vacuum tube heat transfer simplified model

Abstract This paper addresses the design of an algorithm that estimates the irradiance and the selective layer temperature of the all-glass vacuum tube as a function of the ambient temperature in Bucaramanga, Northern Colombia. A Matlab code to meteorological National Aeronautics and Space Administration data processing and to find a linear relationship between the irradiance and ambient temperature on the location of study, was developed. The behavior of this relationship for several hours of the day was studied, and it was found that it is possible to use this relationship as a reliable preliminary approach in water heating systems design using all-glass vacuum tube. This facilitates the design calculations and would encourage the use of solar energy in water heating systems in Colombia.
On Determinability of a Quotient Divisible Abelian Group of Rank 1 by Its Automorphism Group

A criterion for determinability of a quotient divisible Abelian group of rank 1 by its automorphism group in the class of quotient divisible Abelian groups of rank 1 is considered in the paper
Efficient and Robust Learning on Elaborated Gaits with Curriculum Learning

Developing efficient walking gaits for biomechanical robots is a difficult task that requires optimizing parameters in a continuous, multidimensional space. In this paper we present a new framework for learning complex gaits with musculoskeletal models. We use Deep Deterministic Policy Gradient which is driven by the external control command, and apply curriculum learning to acquire a reasonable starting policy. We accelerate the learning process with large-scale distributed training and bootstrapped deep exploration paradigm. As a result, our approach won the NeurIPS 2018: AI for Prosthetics competition, scoring more than 30 points than the second placed solution.
IMSpec: An Extensible Approach to Exploring the Incorrect Usage of APIs

Application Programming Interfaces (APIs) usually have usage constraints, such as call conditions or call orders. Incorrect usage of these constraints, called API misuse, will result in system crashes, bugs, and even security problems. It is crucial to detect such misuses early in the development process. Though many approaches have been proposed over the last years, recent studies show that API misuses are still prevalent, especially the ones specific to individual projects. In this paper, we strive to improve current API-misuse detection capability for large-scale C programs. First, We propose IMSpec, a lightweight domain-specific language enabling developers to specify API usage constraints in three different aspects (i.e., parameter validation, error handling, and causal calling), which are the majority of API-misuse bugs. Then, we have tailored a constraint guided static analysis engine to automatically parse IMSpec rules and detect API-misuse bugs with rich semantics. We evaluate our approach on widely used benchmarks and real-world projects. The results show that our easily extensible approach performs better than state-of-the-art tools. We also discover 19 previously unknown bugs in real-world open-source projects, all of which have been confirmed by the corresponding developers.
Bilingual Textual Similarity in Scientific Documents

Maps of science visualizing the structure of science help us analyze the current spread of science, technology, and innovation (ST&I). ST&I enterprises can use the maps of science as competitive technical intelligence to anticipate changes, especially those initiated in their immediate vicinity. Research laboratories and universities can understand their environmental changes and use the map for their research management. However, traditional maps based on bibliometrics, such as citation and cocitation, have difficulty in representing recently published papers and ongoing projects that have few or no references; thus, maps based on contents, i.e., text-mining, have been developed in recent years for locating research papers/projects, for example, using word and paragraph vectors. The content-based maps, however, still pose difficulty in comparing documents in different languages. Therefore, aiming to construct a bilingual (English and Japanese) content-based map of science for the analyses of ST&I information resources in different languages, this article proposes a method for creating word and paragraph vectors corresponding to bilingual textual information in the same multidimensional space. In a comparison of 11 methods for generating document vectors, we confirmed that the best method achieved 87% accuracy of the bilingual content matching based on 10$\,$000 IEEE papers. Finally, we published a map of approximately 150$\,$000 funding projects of the National Science Foundation, Japan Society for the Promotion of Science, and Japan Science and Technology agency from 2013 to 2017.
Legal regulation peculiarities of the implementation of educational and enlightenment projects as the direction of the information space formation of knowledge

The article is devoted to the peculiarities of the legal analysis of the implementation of educational and enlightenment projects in terms of the information space formation. The aims of the work were to carry out the legal analysis of the terminology of the notions “educational project” and “enlightenment project” in the legal acts of regulation the mechanism of educational policy, justification of the necessity for unification of the approaches in the implementation of educational programs and development of the exact criteria of the evaluation for carrying out the educational and enlightenment projects in terms of information space of knowledge. The conducted analysis showed that the conditions for the functioning of the electronic informational and educational environment are created by numerous Internet projects, search systems, newsletters, forums, online consultations, audio-visual services, social services in the Web 2.0 format, user content and information resources (of public and scientific-methodological nature). The main types of educational projects implemented by means creating conditions for the information space formation of knowledge are identified: media and social educational projects (public media projects), enlightenment projects in the Internet space (enlightenment projects with the use of the possibilities of mass media communications, interactive enlightenment projects) and information enlightenment projects.
Centerness Peak Based Clustering and Image Segmentation

The density peak based clustering algorithm is presented by assuming that cluster centers are local density peaks, and utilizes local density relationship to detect cluster centers. This algorithm has been shown to be effective and efficient in some experiments. However, by studying the clustering mechanism in depth, we find that it may not be appropriate to treat density peaks as cluster centers in some cases. On one hand, the cluster centers obtained this way are often inconsistent with human intuition. On the other hand, local density difference across clusters is likely to influence the cluster center identification result. To relieve this problem, we present centerness as an alternative criterion of cluster center detection. The centerness criterion reflects to which degree the neighborhood of one data is filled with the nearest neighbors evenly, and is calculated with a histogram based method in our approach. By selecting cluster centers from centerness peaks, the clustering can be accomplished in a similar way as density peak algorithm. Our approach relieves the aforementioned problems of density peak algorithm, and performs well in experiments with synthetic and real datasets.
Analytical Modelling and Traffic Control Analysis of Security Attacks for Cloud Computing

Cloud computing technology is a hot topic and popular area similar to the other technologies such as grid computing, parallel computing etc. but the cloud computing idea created back in the 1960s. Most companies start to use cloud computing technologies due to their economic advantage with easy to store data in the business environment. Because of those reasons, the idea of cloud computing organized as a public utility, like paying online water and electricity bills. Apart from these advantages, cloud computing technology has some vulnerabilities, but the most important one is security challenges and issues. The aim of this paper is to analyze the traffic management when the cloud computing system is under security attacks. These security attacks can be any threats in this paper that makes the system as well as the servers unreachable for a certain time. Analytical modeling and evaluation are considered to get QoS measurements under security attacks in this paper. The traffic control management is also proposed and considered together with the analytical model which is the main focus. The resulting analytical model solved using successive over-relaxation (SOR) approach to get QoS measurements. In addition, the analytical results are validated by the discrete event simulation. The evaluation of the system performance in this paper done by analyzing mean queue length, mean response time and blocking probability of the cloud system considering traffic control management when there are security attacks in the system.
Brazil and Colombia virtual exchange project: the Brazilian view

The objective of this paper is to report the preliminary results of a telecollaborative project between a Brazilian university (UTFPR) and a Colombian university (Universidad de Pamplona) that both offer a Licentiate degree in English. The project had two purposes: a pedagogical one to show student-teachers how they can develop their communication skills in English in a collaborative way by sharing information with other student-teachers from a different culture, and a technological one to put future teachers in contact with some technological tools. The paper is divided into three parts: (1) a brief description of the context and the participants; (2) the project itself – the first steps, the objectives, and the tasks; and (3) the Brazilian view of the whole process/project as well as the students’ feedback.
How Can Marketing Managers Thrive in the Age of Artificial Intelligence?

Artificial intelligence (AI) is becoming increasingly important in solving many tasks, which were performed before by humans, that is, translating languages as well as in the hiring process and customer service. The AI technology is improving in a fast manner. This has led to the current debate whether AI is entirely beneficial or should be seen as a threat for the job security. In any way, we are now at the point where nearly everyone is talking and thinking about the application of AI within their work structures. Therefore, we will examine whether AI has the potential to substitute managerial tasks in the way that marketing managers exclusively rely on the respective recommendations and results for a certain action. Additionally, we will discover which tasks lead to positive or negative feelings regarding each manager’s preference. These results will be used to explain the tendency to replace certain activities with AI.
Using Transfer Learning to Detect Phishing in Countries with a Small Population

An increasing number of people are using social media services and with it comes a more attractive outlet for phishing attacks. Phishers curate tweets that lead users to websites that download malware. This is a major issue as phishers can gain access to the user’s digital identity and perform malicious acts. Phishing attacks also have a potential to be similar in different regions, perhaps at different time periods. We investigate the use of transfer learning to detect phishing models learned in one region to detect phishing in other regions. We use a semi-supervised algorithm to train a model on a US based dataset that we then apply to New Zealand. First, we evaluate how effectively transfer learning can be used in different regions to detect potential phishing attacks on online social networks in real time. Secondly, we investigate the different phishing attacks and discuss the differences in phishing attack features detected for different countries. We have collected a real world Twitter dataset over 6 months and show that we are able to detect phishing successfully using US phishing models despite only a low level of phishing occurring in smaller populations such as New Zealand.
Data fusion strategy to improve the realiability of machine learning based classifications

Automatic object recognition plays a major role in many industrial applications. This task is mostly performed by using optical sensors and image processing methods. Degeneration processes, such as surface wear, however, can pose quite some challenges when it comes to high-quality optical recognition. In this article we present our solution to optical character recognition of strongly degenerated numbers, characterized by a varying embossing depth and texture intensity, imprinted on metal surfaces. Under these conditions Machine Learning (ML) based recognition models seem to perform better than conventional ones. Typically, ML models have a black box character in the sense that the algorithm steps have no direct interpretable meaning and are kind of arbitrary. Consequently, the results of such models are difficult to interpret with respect to their trustworthiness. In order to receive more reliable recognition results, we have developed a rule-based fusion strategy that combines the output of several different AI models. This approach not only leads to a higher rate of correctly recognized objects, it also indicates when the recognition result is uncertain. As a result, our method increases the process safety and makes object recognition in industrial applications more flexible and robust.
Clustering-Anonymity Method for Privacy Preserving Table Data Sharing

In the era of big data, the open sharing of government data has increasingly attracted the attention of governments. However, there is privacy leakage risk in the government’s data sharing. For the scene of sharing the table data, this paper proposes a approach for privacy-preserving data sharing in this paper based on anonymity clustering. Firstly, we preprocess the data table, and the records in the table are clustered by k-mediods clustering algorithm. The data table is divided into multiple sub-tables according to the distance between records. Then, the data records in the sub-table are divided based on the information loss parameter value, and the anonymous table data is adjusted so that the sensitive attribute values in the equivalence class are different. Last, Laplace noises are added to the value of sensitive attribute to ensure the privacy of the shared data. Compared with the classical k-anonymous MDAV algorithm in execute time, information loss and information entropy, the experimental results show that the proposed algorithm can reduce the operating time, improve the privacy protection to some extent, and has certain availability from the three aspects.
Existential Types for Relaxed Noninterference

Information-flow security type systems ensure confidentiality by enforcing noninterference: a program cannot leak private data to public channels. However, in practice, programs need to selectively declassify information about private data. Several approaches have provided a notion of relaxed noninterference supporting selective and expressive declassification while retaining a formal security property. The labels-as-functions approach provides relaxed noninterference by means of declassification policies expressed as functions. The labels-as-types approach expresses declassification policies using type abstraction and faceted types, a pair of types representing the secret and public facets of values. The original proposal of labels-as-types is formulated in an object-oriented setting where type abstraction is realized by subtyping. The object-oriented approach however suffers from limitations due to its receiver-centric paradigm. In this work, we consider an alternative approach to labels-as-types, applicable in non-object-oriented languages, which allows us to express advanced declassification policies, such as extrinsic policies, based on a different form of type abstraction: existential types. An existential type exposes abstract types and operations on these; we leverage this abstraction mechanism to express secrets that can be declassified using the provided operations. We formalize the approach in a core functional calculus with existential types, define existential relaxed noninterference, and prove that well-typed programs satisfy this form of type-based relaxed noninterference.
Application of stochastic and evolutionary optimization algorithms to base case problems

Stochastic and evolutionary optimization methods are more and more used in the analysis, design, and operation of systems that are highly nonlinear, high dimensional, and noisy or for problems that are not easily solved by classical deterministic methods of optimization. This chapter primarily concentrates on evaluating the results of various stochastic and evolutionary optimization algorithms such as genetic algorithm simulated annealing, differential evolution, ant colony optimization, particle swarm optimization, and artificial bee colony algorithm by applying them to different base case problems. Three typical base case problems of different complexities are used for implementation and evaluation of these methods. For the comparative performance, these methods are studied in terms of different criteria such as speed of convergence, number of iterations required to obtain the final converged solution, and complexity of model equation. The solution strategies and the analysis of results on application to base case nonlinear optimization problems of this chapter lead to extend the usefulness of these methods to solve real and complex optimization problems of different domains.