Reviewerly/exHarmony huggingface dataset:
Contradict the Machine: A Hybrid Approach to Identifying Unknown Unknowns

Machine predictions that are highly confident yet incorrect, i.e. unknown unknowns, are crucial errors to identify, especially in high-stakes settings like medicine or law. We describe a hybrid approach to identifying unknown unknowns that combines the previous algorithmic and crowdsourcing strategies. Our method uses a set of decision rules to approximate how the model makes high confidence predictions. We present the rules to crowd workers, and challenge them to generate instances that contradict the rules. To select the most promising rule to next present to workers, we use a multi-armed bandit algorithm. We evaluate our method by conducting a user study on Amazon Mechanical Turk. Experimental results on three datasets indicate that our approach discovers unknown unknowns more efficiently than state-of-the-art baselines.
Overview of Recommendation Systems

In recent years, the web dominates internet users. To ensure the satisfaction of these later, a large amount of data is generated in various fields. However, the web suffers from a great deal of information overload. This issue makes the user perplexed in choosing the resource that meets his needs. Recommendation Systems (RSs) have emerged as an unavoidable solution to address information overload. This offer the user the most appropriate resource that meets his profile. The most common techniques for filtering irrelevant resources fall into three categories: Collaborative Filtering (CF), Content-Based filtering (CB), and hybrid filtering. In spite of their success, these techniques remain limited as the user requirements increase. To overcome the limitations of traditional techniques, several approaches have been developed. In the present paper, we establish the state of the art of recommendation systems, including the current work. It serves as a compass for researchers. This allows them to have a clear idea of the existent in order to easily propose an effective solution. A part of this paper is devoted to the recommendation systems carried out for the educational purpose and support smart education.
W!NCE

The ability to unobtrusively and continuously monitor one's facial expressions has implications for a variety of application domains ranging from affective computing to health-care and the entertainment industry The standard Facial Action Coding System (FACS) along with camera based methods have been shown to provide objective indicators of facial expressions; however, these approaches can also be fairly limited for mobile applications due to privacy concerns and awkward positioning of the camera. To bridge this gap, W!NCE re-purposes a commercially available Electrooculography-based eyeglass (J!NS MEME) for continuously and unobtrusively sensing of upper facial action units with high fidelity. W!NCE detects facial gestures using a two-stage processing pipeline involving motion artifact removal and facial action detection. We validate our system's applicability through extensive evaluation on data from 17 users under stationary and ambulatory settings.
The Effect of Mobile Phone Education for the Elderly to Eliminate the Digital Divide between Generations

이 연구는 2007년부터 SK텔레콤의 지원으로 진행하고 있는 노인 휴대폰 활용교육 프로그램 ‘세대공감 행복한 모바일 세상’의 세대 간 정보격차 해소 및 세대통합 효과 및 의미를 조명해 보고자 하였다. 이를 위하여 교육 효과성 평가를 시작했던 2009년부터 2016년까지 노인 4,615명과 대학생 봉사자 3,011명 등 총 7,626명의 자료를 분석하였다. 노인들의 휴대폰 사용법의 이해정도, 스마트폰 주요 기능의 활용정도, 생활 만족도, 자아존중감, 자기효능감 등 노년기 삶의 질 지표, 그리고 노인과 대학생의 상대 세대에 대한 태도를 사전-사후로 비교하였다. 분석 결과 첫째, 참여 노인들의 휴대폰 사용 방법에 대한 이해가 교육 전에 비하여 교육 후에 유의미하게 향상되었다. 또 스마트폰의 주요 기능들에 대한 활용정도 또한 교육 후 유의미하게 향상되었다. 둘째, 참여 노인들의 삶의 질 즉 생활만족도, 자아존중감, 자기효능감 역시 교육 전에 비하여 교육 후에 유의미한 향상을 보였다. 셋째, 교육 전보다 교육 후 노인의 청년에 대한 태도는 물론이고 대학생의 노인에 대한 태도 또한 유의미하게 더 긍정적으로 변화하였다. 결론적으로 이 연구를 통하여 노인 휴대폰 활용교육이 세대 간 정보격차 해소뿐 아니라 노년기 삶의 질 향상과 세대통합에 효과가 있음을 확인할 수 있었다.The purpose of this study was to investigate the effect and meaning of the elderly mobile phone education program Generation Sympathy, Happy Mobile World supported by SK Telecom. For this purpose, a total of 7,626 data were analyzed, including 4,615 older adults and 3,011 college volunteers from 2009 to 2016, when the evaluation of educational effectiveness was carried. The elderly s understanding of mobile phone usage, the degree of utilization of smart phone s main functions, life satisfaction, self-esteem, self-efficacy, attitudes toward youth and college students attitudes toward the elderly were compared before and after. The results of this study were as follows: ① The elderly participants’ understanding how to use mobile phone significantly improved after education than before. In addition, their degree of utilization of the main functions of the smart-phone has also improved significantly after the training than before. ② Quality of life including life satisfaction, self-esteem and self-efficacy of elderly participants also showed significant improvement after education than before. ③ Both of the elderly participants‘ attitudes toward of the youth and the college volunteers’attitudes toward the elderly changed significantly more positively. In conclusion, we confirmed that elderly mobile phone education is effective in eliminating the digital divide between generations, improving quality of life in the elderly, and integrating generations.
Selective Untargeted Evasion Attack: An adversarial example that will not be classified as certain avoided classes

Deep neural networks (DNNs) have useful applications in machine learning tasks involving recognition and pattern analysis. Despite the favorable applications of DNNs, these systems can be exploited by adversarial examples. An adversarial example, which is created by adding a small amount of noise to an original sample, can cause misclassification by the DNN. Under specific circumstances, it may be necessary to create a selective untargeted adversarial example that will not be classified as certain avoided classes. Such is the case, for example, if a modified tank cover can cause misclassification by a DNN, but the bandit equipped with the DNN must misclassify the modified tank as a class other than certain avoided classes, such as a tank, armored vehicle, or self-propelled gun. That is, selective untargeted adversarial examples are needed that will not be perceived as certain classes, such as tanks, armored vehicles, or self-propelled guns. In this study, we propose a selective untargeted adversarial example that exhibits 100% attack success with minimum distortions. The proposed scheme creates a selective untargeted adversarial example that will not be classified as certain avoided classes while minimizing distortions in the original sample. To generate untargeted adversarial examples, a transformation is performed to minimize the probability of certain avoided classes and distortions in the original sample. As experimental datasets, we used MNIST and CIFAR-10, including the Tensorflow library. The experimental results demonstrate that the proposed scheme creates a selective untargeted adversarial example that exhibits 100% attack success with minimum distortions (1.325 and 34.762 for MNIST and CIFAR-10, respectively).
An Empirical Study on the Usefulness, Effectiveness and Practicability of Vocational and Professional Education and Training’s (VPET) Open Educational Resources (OER) in the Hotel Industry

Open educational resources (OER) is increasingly popular amongst all levels of educational institutions. However, it is noticed that adaptability of OERs for in-service training in specific industry is a major factor. Training managers, trainers and trainees also have concerns on the training packages’ practicability developed by vocational and professional education and training’s (VPET) subject teachers and academics. Although advice and consultancy were obtained from industry practitioners during the development of the training packages, it is always an issue of generalisation and adaptability because of the difficulty for one jacket to fits all. Each organisation has its training practices in terms of organisational culture, protocol and standard. This empirical study looks into the aforementioned factors with a hope to provide solutions and insights on future development, and application of OERs in the hotel industry. Using the concept of expert judgement, feedback and comments were gained from trainers and trainees on the following areas (1) instructional design, (2) user interface and (3) functionality as well as qualitative feedback on the usefulness and adaptability to suit industry’s training needs to examine the effectiveness and practicability of the training packages developed for a VPET OER Project. Findings revealed that trainers and trainees in the hotel industry well accepted the OERs and regarded they are practical, comprehensive and well designed to meet their needs. Feedback also showed that the training materials are very useful especially for those learners who are new to the hotel industry.
Corrections to “Accurate Cervical Cell Segmentation From Overlapping Clumps in Pap Smear Images” [Jan 17 288-300]

In <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , Baiying Lei was indicated as the corresponding author. Tianfu Wang and Baiying Lei should have been indicated as the corresponding authors.
&lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$\textsf{DWCDM+}$\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;: A BBB secure nonce based MAC

In CRYPTO 2016, Cogliati and Seurin have proposed a nonce-based MAC called Encrypted Wegman-Carter with Davies-Meyer ($\textsf{EWCDM}$), from an $n$-bit block cipher $\textsf{E}$ and an $n$-bit almost xor universal hash function$\textsf{H}$ as \begin{document}$\textsf{E}_{K_2}\bigl(\textsf{E}_{K_1}(N)\oplus N\oplus \textsf{H}_{K_h}(M)\bigr),$ \end{document} for a nonce $N$ and a message $M$ that provides roughly $2n/3$-bit MAC security. However, obtaining the similar security using a single block cipher key was posed as an open research problem. In this paper, we present Decrypted Wegman-Carter with Davies-Meyer ($\textsf{DWCDM+}$) construction based on a single block cipher key that provides $2n/3$-bit MAC security from an $n$-bit block cipher $\textsf{E}$ and an $n$-bit $k$-regular ($\forall k \leq n$), almost xor universal hash function $\textsf{H}$ as \begin{document} $ \textsf{E}^{-1}_{K}\bigl(\textsf{E}_{K}(N)\oplus N \oplus \textsf{H}_{K_h}(M)\bigr). $ \end{document} $\textsf{DWCDM+}$ is structurally very similar to its predecessor $\textsf{EWCDM}$ except that the facts that (i) the number of block cipher keys reduced from $2$ to $1$ and (ⅱ) the outer encryption call is replaced by a decryption one. To make the construction truely single-keyed, here we derive the hash key $K_h$ as the block cipher output of a fixed string $0^{n-2} \| 10$ as long as the hash key is of $n$ bits. We show that if the nonce space is restricted to $(n-1)$ bits, $\textsf{DWCDM+}$ is secured roughly up to $2^{2n/3}$ MAC queries ($2^{n/2}$ MAC queries) and $2^n$ verification queries against nonce respecting (nonce misuse resp.) adversaries.
Firebase as BaaS for College Android Application

In the present times, words like 'mobile application' and 'cloud' are making a significant mix.Every day there is some new application in the market with the point of conveying the best execution to their clients.Picking the correct BaaS (Backend as a Service) is vital and it comes down to what your project requires.A sturdy infrastructure is built with mobile backend services such as with Firebase which aids developers by handling the backend of these applications.In this paper, the Android application MCCApp utilizes the features provided by Firebase to show its usage as the best Mobile Backend-as-a-service.
Multiple linked-view exploration on large displays facilitated by a secondary handheld device

Large displays are capable of visualising a large amount of data on multiple views including scatterplots and parallel coordinates and are often present in meeting rooms. They can be used to interact with a dataset and foster discussion among team members. Although some of these large screens have multi-touch capabilities, in many cases it is cumbersome to have to stand close to the display in order to interact with it. One of the solutions is to use a small handheld display to interact with the large display. This paper discusses how traditional interactions such as selection, brushing, and linking can be performed using a secondary handheld device. As a proof of concept, a system including scatterplots and parallel coordinates views is implemented. The interactions are straightforward and are useful for any interactive visual analysis application on a large display with wireless connectivity.
Efficient Analysis of Vertical Projection Histogram to Segment Arabic Handwritten Characters

The paper discusses the segmentation of words into characters, which is an essential task in the development process of character recognition systems, as poorly segmented characters will automatically be unrecognized. The segmentation of offline handwritten Arabic text poses a greater challenge because of its cursive nature and different writing styles. In this article, we propose a new approach to segment handwritten Arabic characters using an efficient analysis of the vertical projection histogram. Our approach was tested using a set of handwritten Arabic words from the IFN/ENIT database, and promising results were obtained.
An Intercultural Approach to Multimodality in Selected German, Anglo-American, Italian and Turkish Cosmetics Advertisements

Multimodality is a term that refers to the co-presence and mutual linking of different semiotic resources at different levels in communication.It refers to the diverse ways in which a combination of verbal, visual and non-verbal elements are used in meaning making.Nowadays various media such as TVs, mobile phones, computers, tablets or the internet provide for the creation of new text forms which include a variety of semiotic modes.As a result of this, today's texts contain complex modalities like visual images or auditory elements.Therefore, texts are seen as multimodal structures that combine different semiotic modes such as language, image, music, sound and typography.Advertisements are one of the most ideal multimodal text types because of their potential for combining and integrating the highest level of multiple semiotic modalities.Thus, they are one of the most analyzed text types in multimodal oriented text researches.Therefore, the aim of this study is to analyze and compare German, Anglo-American, Italian and Turkish print advertisements with each other.Moving towards a multimodal oriented text analysis theory, this study attempts to demonstrate the multimodal structure of the selected advertisements in four languages and how the modalities are used to express meaning. ABSTRACT (ENGLISH)Multimodality is a term, which refers to the co-presence and mutual linking of multiple semiotic resources at different levels in communication.Nowadays the various media such as TV, mobile phone or internet provide to create new text forms which include a variety of semiotic modes.As a result of this, today's texts contain complex modalities.Texts are therefore seen as multimodal structures, which combine various semiotic resources such as language, image, music, sound and typography.Advertisements are one of the most ideal multimodal text examples because of their potential of combining and integrating highest levels of different semiotic modalities.Advertisements are therefore one of the most analyzed text types in multimodal oriented text researches.The aim of this study is to analyze and compare German, Anglo-American, Italian and Turkish print advertisements moving towards a multimodal oriented method, which is based on text linguistics.The corpus is comprised of eight cosmetics advertisements of different international brands, which were published in German, American, Italian and Turkish women's magazines in 2016.The analyzed advertisements are derived from the following magazines: Cosmopolitan (German Edition), Vogue (USA Edition), Silhouette Donna (Italy) und Elele (Turkey).The analysis is based on multimodal oriented text analysis models of Angelika Hennecke (1999), Nina Janich (2010) and Hartmut Stöckl (2011).Furthermore the analysis include text extern and text intern criteria.Framed in multimodal analysis and intercultural comparison this paper presents the similarities and differences between these print advertisements in four different languages.
SSN-SPARKS at SemEval-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented Data

This paper describes the work on mining the suggestions from online reviews and forums.Opinion mining detects whether the comments are positive, negative or neutral, while suggestion mining explores the review content for the possible tips or advice.The system developed by SSN-SPARKS team in SemEval-2019 for task 9 (suggestion mining) uses a rule-based approach for feature selection, SMOTE technique for data augmentation and deep learning technique (Convolutional Neural Network) for classification.We have compared the results with Random Forest classifier (RF) and Multi-Layer Perceptron (MLP) model.Results show that the CNN model performs better than other models for both the subtasks.
SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions

This paper describes the participation of the SINAI-DL team at RumourEval (Task 7 in Se-mEval 2019, subtask A: SDQC).SDQC addresses the challenge of rumour stance classification as an indirect way of identifying potential rumours.Given a tweet with several replies, our system classifies each reply into either supporting, denying, questioning or commenting on the underlying rumours.We have applied data augmentation, temporal expressions labeling and transfer learning with a four-layer neural classifier.We achieve an accuracy of 0.715 with the official run over reply tweets.
Vision and Crowdsensing Technology for an Optimal Response in Physical-Security

Law enforcement agencies and private security companies work to prevent, detect and counteract any threat with the resources they have, including alarms and video surveillance. Even so, there are still terrorist attacks or shootings in schools in which armed people move around a venue exercising violence and generating victims, showing the limitations of current systems. For example, they force security agents to monitor continuously all the images coming from the installed cameras, and potential victims nearby are not aware of the danger until someone triggers a general alarm, which also does not give them information on what to do to protect themselves. In this article we present a project that is being developed to apply the latest technologies in early threat detection and optimal response. The system is based on the automatic processing of video surveillance images to detect weapons and a mobile app that serves both for detection through the analysis of mobile device sensors, and to send users personalised and dynamic indications. The objective is to react in the shortest possible time and minimise the damage suffered.
Commutative Ideals of BCK-Algebras Based on Uni-Hesitant Fuzzy Set Theory

The notion of uni-hesitant fuzzy commutative ideals in BCK-algebras is introduced, and related properties are investigated. Relations between uni-hesitant fuzzy commutative ideals and uni-hesitant fuzzy ideals are discussed. Characterizations of uni-hesitant fuzzy commutative ideals are considered. Conditions for a uni-hesitant fuzzy ideal to be a uni-hesitant fuzzy commutative ideal are provided. Extension property for a uni-hesitant fuzzy commutative ideal is established.
Particle swarm optimization based multi-task parallel reinforcementlearning algorithm

Transfer learning has been identified as conducive to improving the speed of machine learning in many areas. In multi-task reinforcement learning, transfer learning can assist the transfer of experiences between different tasks. The research conducted in this article is focused on two aspects. On t he one hand, multi-task parallel transfer learning can improve the learning speed of parallel learning tasks. On the other hand, the learning of the current optimal experience can help the target point rewards to be transmitted to the starting point. The value of this self-learning can also accelerate the convergence speed of the reinforcement learning. According to the research into these two aspects, this paper uses the idea of particle swarm optimization (PSO) to conduct self-learning and interactive learning in multi-task parallel learning. In this paper, a new multi-task learning algorithm named PSO-MTPRL (Multi-Task Parallel Reinforcement Learning based on PSO) is proposed. Based on the idea of PSO algorithm, the Boltzmann strategy, Self-Learning Process (SLP) and Interactive Learning Process (ILP) are selected probabilistically. Based on the characteristic exhibited by reinforcement learning, segmented learning model is recommended. In the early learning stages, the complete Boltzmann exploration strategy is applied, and B-SLP-ILP (Boltzmann-SLP- ILP) learning procedure is conducted exclusively in the middle stage of the learning. In the late learning stages, Boltzmann exploration is involved again. The segmented learning model can help ensure the balance of the exploration and exploitation, in addition to ensuring that all tasks convergence.
Avebury Portal – A Location-Based Augmented Reality Treasure Hunt for Archaeological Sites

Many archaeological sites are less popular by visits amongst the younger group and overall less popular than majority of other heritage sites. They are often not enhanced by supporting medium like in museums or historic buildings. Many augmented reality (AR) systems have been developed for archaeological sites and proved to benefit user engagement. However, most result in the superimposition of virtual reconstructions of the site and very little interaction. In this paper, we demonstrate the development of a location-based treasure hunt AR app, Avebury Portal, for the heritage site; Avebury in England. Avebury Portal uses puzzles with the environment to give clues, and a narrative that responds to the user’s location. We developed Avebury Portal with Unity Engine and Vuforia to demonstrate the effectiveness of using AR to enhance visitors’ experiences on learning.