Reviewerly/exHarmony huggingface dataset:
<i>Place Your Bets</i>: The Future of Voice Interfaces to Intelligent Assistants

The AI Bookie column documents highlights from AI Bets, an online forum for the creation of adjudicatable predictions, in the form of bets, about the future of AI. While it is easy to make broad, generalized, or off‐the‐cuff predictions about the future, it is more difficult to develop predictions that are carefully thought out, concrete, and measurable. This forum was created to help researchers craft predictions whose accuracy can be clearly and unambiguously judged when the bets come due. The bets will be documented both online and regularly in this column. We encourage bets that are rigorously and scientifically argued. We discourage bets that are too general to be evaluated or too specific to an individual or institution. The goal is not to continue to feed the media frenzy and outsized pundit predictions about AI, but rather to curate and promote bets whose outcomes will provide useful feedback to the scientific community. For detailed guidelines and to place bets, visit sciencebets.org .
The First Step towards Automatic Quality Evaluation of Chinese Vowel Pronunciations for Foreign Learners for Self-training

Nowadays, computer-assisted language learning (CALL) systems are widely used for language education.Since the pronunciation of Chinese is difficult, it is important to build a system to evaluate a learner's pronunciation in a real-time fashion so as to maintain the motivation of learning.This study tried to develop such a system that not only judges pronunciations from the viewpoint of acoustic phonetics, but also provides a learner an advice on improving his/her pronunciations.As the first step, we built a system for Chinese monophthong vowels, and analyzed the acoustic features of the pronunciations between Chinese and Japanese.The results show that it is possible to distinguish the characteristics of pronunciation using the formant frequency, which is one of the acoustic features; and it is also possible to distinguish round and unround lips, which has been difficult, by using three kinds of formant frequencies from the first formant to third.
Hybrid Method for Copy-Move Forgery Detection in Digital Images

Digital image authenticity is significant in many social areas. Image forgery detection becomes a challenging task. Copy-move forgery is one of the tampering techniques which is frequently used, part of the image is copied and pasted to other parts of the same image. This paper proposes a new method for copy-move forgery detection. Proposed method integrates both block-based and keypoint-based forgery detection. Host image is first divided into blocks and keypoints are extracted from each image block. Blocks are compared based on the keypoints in them. Number of similar keypoints identified from a pair of blocks exceeds a preset threshold, then those block pair is matched. Matched blocks are considered as the forged region and Output is displayed after neighbour pixel merging and morphology operations. The accuracy of the method is calculated and analysed with different images.
Research on Intelligent Decision of Pulmonary Tuberculosis Disease Based on Data Mining

Aiming at the problem that the low diagnostic efficiency and low accuracy of the single data mining method for Diagnosis of pulmonary tuberculosis, In this study, the electronic records of 1203 cases of tuberculosis patients in Changping District City, Beijing City of Beijng and Beijing Institute of tuberculosis control and tuberculosis control were build, Tuberculosis disease diagnosis model is built by application of rough set and decision tree method, On the basis of this, the diagnosis system of pulmonary tuberculosis was constructed. In this study, the combining method of rough set and decision tree was approached to attribute reduction, the model reduced redundant 57 attributes and remained 22 attributes, and articled 7 the decision rules. The model accuracy is 89.46%. Compared with the non reduction method, the decision rule was reduced by 128%, and the accuracy of the model remained unchanged. The research results showed that the algorithm can reduce the time and space complexity of the algorithm while ensuring the accuracy of the model, so as to improve the efficiency of the mining, and provide some references for clinical diagnosis.
Assessing Model Performance with Generalization Tests

This chapter focuses on the problem of model evaluation, and it is separated into three parts. First, we discuss how to assess model fit to the existing data. Second, we discuss methods of assessing the model’s generalizability to new data. Third, we present the details necessary to carry out several methods of model assessment.
Activities of Technical Committee on Electromagnetic Theory and its Research Topics

This report briefly presents activities of Technical Committee on Electromagnetic Theory (EMT), IEEJ. Our committee is now implementing the activities on 2018, which includes three times of the technical committee meeting and four times of technical committee conference. The report also contains three research topics presented in the technical committee meeting on EMT.
Sentimental Analysis Tools

Opinion mining, also known as sentimental analysis, is the analysis of sentiment (emotion, affection, experience) towards the target object. In the present era, everyone is interested to know the opinions of others before making a decision or performing a task. Hence, it is necessary to collect the information (features) from relatives, friends, or web. These opinions or feedbacks help them to decide their action. With the advent of social media and use of digital technologies, web is a huge resource for data. However, it is time-consuming to read the data collected from the web and analyze it to arrive at informed decisions. This chapter provides complete overview of tools to simplify the operations of opinion mining like data collection, data cleaning, and visualization of predicted sentiment.
An Efficient and Revocable Decentralizing Attribute-Based Encryption for Mobile Cloud Computing

Mobile cloud computing (MCC) is becoming an important way of data sharing. It is of great value for people to store and retrieve personal data at anytime and anywhere. Attribute-based encryption (ABE) can solve the problem of flexible sharing among multiple users in traditional encryption, but it cannot apply to mobile clients directly because of its low efficiency. How to meet the people’s practical application needs and to control and manage the personal data safely and flexibly is a concrete embodiment of the security problem after the cloud computing is mobile. In this paper, an efficient and revocable decentralizing attribute-based encryption scheme for mobile cloud environment is proposed. In the scheme, it does not have the upper limit of the total attribute, and without the central authority (CA), each attribute authority generates private key independently with the users. In addition, the linear secret-sharing scheme (LSSS) is used to construct the access structure with a high flexibility. The method of precomputing and outsourcing can reduce the computation cost of the user side. Besides, the scheme is proved to be static secure and support revocation under the random oracle model. Compared to the existing related schemes, the proposed scheme is more practical and effective in mobile cloud environment.
Automated Random Color Keypad

In early 1970s, automated teller machine came into existence which was placement for cash counters at banks. People could now do transactions 24X7 with ease. But as ATM expanded its reach into human life, crimes related to ATM theft and fraud increased exponentially. There are flaws related to ATM security which is exploited by the criminals. ATM card cloning, card skimming, ATM pin theft are few of the most common crimes related to the ATM. These crimes result in loss of money measured in billions. The newspapers are full of these crime reports and hacks. In this paper, we have proposed an automated random colour keypad by which ATM security can be enhanced; making these ATM attacks and fraud very difficult. This ARC keypad will act as a secure replacement for traditional ATM keypad. Several experimental results of the ARC keypad have been included in this paper which proves that our mechanism enhances ATM pin security and reduces the chances of fraud to a great extent.
An Analysis of IPv6 Protocol Implementation for Secure Data Transfer in Cloud Computing

Information surveillance and approach subordination were superior among the majority ungovernable determined research endeavors in cloud cipher, in perspective of clients deploying their exquisite data to cloud suppliers. Cloud compile maneuvers the client’s records to flexible data focuses that were remotely symphonized, on whichever client has no restraint. It might be significant to apply patterns to establish the impingement in enforcing IPv6 locations on the cloud that might necessitate diversities to routers, terminals, defense strategies, and thus forth. IPAM software tools shall facilitate in scaling IP data to business rationale preferences for instance sectors, preferences, or framework via illustration map and facsimiles of a probable novel groundwork. This article investigates the stumbling blocks and interpretations for realizing an efficient cloud computing environment employing IPv6. This paper delves into cloud computing in an intense elevation that is universally applied to take advantage of IPv6 quickly and effectively, with minimal interruption.
Ideating a Recommender System for Business Growth Using Profit Pattern Mining and Uncertainty Theory

Association rule mining (ARM) alone is a classical yet powerful method for basic rule discovery. However, generic measures being used are insufficient for specific pattern generation and rules of business interest. Critical decision making is a “key” component in contemporary businesses which could be rewarded by periodically utilizing patterns and rules to steer business growth and profit as well. To effectuate self-propelled growth in businesses, a feasible optimal recommender system needs to be accomplished without human intervention that recommends targeted product marketing and promotional strategies. In conjunction to ARM, uncertainty is a growing challenge in data mining research with facets of being probabilistic, fuzzy, or vague. Among many set theories to surmount uncertainty, vague set theory is employed for handling vagueness in data which gives the motivation of implementing a knowledge-based recommender framework by aggregating the two approaches to predict uncertain market growth strategy patterns and profitable rules.
An investigation of the possibility of assessing general language proficiency and reading proficiency in English as a foreign language through scrambled-lettered texts

Reading Scrambled-Lettered Texts (SLT) is a complex activity, related to many factors, including language proficiency. This study investigates the possibility of using SLT as an integrative method to assess language and reading proficiency in English as a Foreign Language (EFL). A SLT reading test in English was applied to ten Brazilian EFL learners and the results were compared to their global and reading scores in the TOEFL ITP. Despite the limitation of the sample size, a negative correlation between the TOEFL scores and the number of errors in the SLT test was found. The results were significant in terms of p-value in four out of six cases and the presence of an outlier suggested an even higher correlation and relevance. A Portuguese SLT test was also applied to the sample, and the participants, who had a similar level of education, presented homogeneous results, contrasting with those found in the English test.
Video-Based Pedestrian Attribute Recognition

In this paper, we first tackle the problem of pedestrian attribute recognition by video-based approach.The challenge mainly lies in spatial and temporal modeling and how to integrating them for effective and dynamic pedestrian representation.To solve this problem, a novel deep recurrent neural network with hybrid pooling strategy is proposed.Since publicly available dataset is rare, a new large-scale video dataset for pedestrian attribute recognition is annotated, on which the effectiveness of both video-based pedestrian attribute recognition and the proposed new network architecture is well demonstrated.
Accountability management for multi-tenant cloud services

The widespread adoption of multi-tenancy in the Software as a Service delivery model triggers several data protection issues that could decrease the tenants' trustworthiness. In this context, accountability can be used to strengthen the trust of tenants in the cloud through providing the reassurance of the processing of personal data hosted in the cloud according to their requirements. In this paper, we propose an approach for the accountability management of multi-tenant cloud services allowing: compliance checking of services behaviours with defined accountability requirements based on monitoring rules, accountability-violation detection otherwise, and post-violation analysis based on evidences. A tool-suite is developed and integrated into a middleware to implement our proposal. Finally, experiments we have carried out show the efficiency of our approach relying on some criteria.
Artificial Intelligence and databases in the age of big machine data

This paper deals with those databases where Artificial Intelligence technologies are used to obtain, verify, or present the database’s contents (‘AI databases’). The overarching research question is whether AI databases can be protected under the copyright and sui generis regimes provided by the Database Directive. The alleged inadequacy of the sui generis right for the data economy and, in particular, for machine-generated data led the European Parliament to call on the Commission to abolish said right and the Commission to propose the introduction of a data producer’s right as a new property that would have done what the sui generis right had been unable to. It is this paper’s contention that, contrary to popular belief, the sui generis right is fit for AI databases and that a different solution would lead to an overprotection of said subject matter by contractual means. The sui generis right may be the best, if not the only, way to protect AI ‘authorial’ works. Indeed, even if AI works currently fall outside the scope of copyright law for lack of originality, they could nonetheless be protected if part of a database. Thus, thanks to AI, the sui generis right may become more important than it ever was.
Linking and copyright – a problem solvable by functional-technical concepts?

Linking as such is nothing new in copyright discussion – a discussion that started in the early 1990s. However, the Internet, its business models and linking activities as part of those models has undergone thorough changes. Indeed, as opposed to early discussion that concerned linking in its simple form in an undeveloped Internet, nowadays it is questionable whether linking can be discussed as a single, discrete phenomenon. Moreover, the role of a link has changed: while its immediate function is to deliver information on the location of a particular entity’s web address, in a broader perspective the claim might be made that linking is also in certain cases used as a tool for de facto content distribution.
Design Spaces in Visual Analytics Based on Goals: Analytical Behaviour, Exploratory Investigation, Information Design &amp; Perceptual Tasks

This paper considers a number of perspectives on design spaces in visual analytics and proposes a new set of four design spaces, based on user goals.Three of the user goals are derived from the literature and are categorised under the terms exploratory investigation, perceptual tasks, and information design.The fourth goal is categorised as analytical behaviour; a recently defined term referring to the study of decision-making facilitated by visual analytics.This paper contributes to the literature on decision-making in visual analytics with a survey of real-world applications within the analytical behaviour design space and by providing a new perspective on design spaces.Central to our analysis is the introduction of decision concepts and theories from economics into a visual analytics context.Given the recent interest in decision-making we wanted to understand the emerging topic of analytical behaviour as a design space and found it necessary to look at more than just decision-making to make a valuable contribution.The result is an initial framework suitable for use in the analysis or design of analytical behaviour applications.
PENERAPAN PEMBELAJARAN KONTEKSTUAL UNTUK MENINGKATKAN HASIL BELAJAR MATEMATIKA SISWA KELAS I SD NEGERI 012 CANDIREJO KECAMATAN PASIR PENYU KABUPATEN INGRAGIRI HULU

This classroom action research was motivated by the low mathematics learning outcomes of class I students, with a class average of 56.25. The purpose of this research was to improve the mathematics learning outcomes of first-grade students at SD Negeri 012 Candirejo Kecamatan Pasir Penyu through the Application of a contextual Teaching Model. The subjects of this study were a class I students totaling 17 people. The results of research in class I SD Negeri 012 Candirejo Kecamatan Pasir Penyu proved that the Application of contextual Teaching Model can improve mathematics learning outcomes in the pre-cycle average value of students is 56.25 in cycle I increased to 67.5 and in cycle II increased to 82, 5.
Different aspects of trade mark confusion with respect to distribution of CAD files in the era of 3D printing

When discussing online content production and distribution, 3D printing (3DP) represents an emerging technology that has the potential to rise to one of the important generators of the next industrial revolution. From a business point of view there are clearly many advantages of 3DP since it makes the manufacturing process much more efficient. The development of 3DP technology will, however, create many kinds of challenges related to intellectual property rights, many of which are of a fundamental nature.
Emerging Approaches and Advances in Big Data

This special issue of Symmetry entitled “Emerging Approaches and Advances in Big Data” consists of 17 papers [...]
Research of Autonomic Learning Ability Evaluation System Based on Rain Classroom

Autonomic learning in network environment has been a major part of blended learning, in order to the feedback of autonomic learning effect. This article presents the evaluation index system of autonomic learning ability based on rain classroom, and evaluates the autonomic learning ability in rain classroom that provides evaluation report for students, and then improves the autonomic learning ability of students as well as the effect of blended teaching.
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to result from the use of different segmentation tools to generate brain volumes. Here, processing pipelines for seven automated tools that can be used to segment grey matter within the brain are presented. The protocol provides an initial step for researchers aiming to find the most accurate method for generating grey matter volumes from T1-weighted MRI scans. Steps to undertake detailed visual quality control are also included in the manuscript. This protocol covers a range of potential segmentation tools and encourages users to compare the performance of these tools within a subset of their data before selecting one to apply to a full cohort. Furthermore, the protocol may be further generalized to the segmentation of other brain regions.
The Double-Accelerated Stochastic Method for Regularized Empirical Risk Minimization

In this paper, we propose a general scheme for the double-accelerated stochastic method (DASM) to minimize the ill-conditioned regularized empirical risk minimization (ERM) problem, which includes the sum of two convex functions: one is the sum of the smooth component functions, and the other is the block separable structure. We use an inner–outer iteration procedure to accelerate such convex functions, which considers the accelerated randomized proximal coordinate gradient method as the inner procedure and the accelerated proximal point algorithm as the outer procedure. In order to make a connection between the inner and outer procedures, we enhance the dependence on the condition number and redefine the regularization parameter to improve runtime and convergence rates. We also incorporate the non-smooth regularization term into the inner accelerated procedure. Furthermore, we give a theoretical analysis of the iteration complexity and runtime, which is justified in practical applications. We show that the proposed DASM has better or comparable performance than current state-of-the-art accelerated methods for the ill-conditioned ERM problem.
Stacks and Queues

This chapter covers stacks and queues; both are versatile data structures commonly used in the implementation of other, more complex data structures. You will learn what stacks and queues are, how and when they are used, and how to implement them. Finally, the exercises will help you to understand these concepts as well as when to apply stacks and queues to an algorithmic problem.