Reviewerly/exHarmony huggingface dataset:
Patterns of Search Result Examination

To determine key factors that affect a user's behavior with search results, we conducted a controlled eye-tracking study of users completing search tasks using both desktop and mobile devices. We focus our investigation on users' behavior from their query to the first action they take with the search engine results page (SERP): either a click on a search result or a reformulation of their query. We found that a user deciding to reformulate a query rather than click on a result is best understood as being caused by the user's examination pattern not including a relevant search result. If a user sees a relevant result, they are very likely to click it. Of note, users do not look at all search results and their examination may be influenced by other factors. The key factors we found to explain a user's examination pattern are: the rank of search results, the user type, and the query quality. While existing research has identified rank and user types as important factors affecting examination patterns, to our knowledge, query quality is a new discovery. We found that user queries can be understood as either of weak or strong quality. Weak queries are those that the user may believe are more likely to fail compared to a strong query, and as a result, we find that users modify their examination patterns to view fewer documents when they issue a weak query, i.e. they give up sooner.
Improving reinforcement learning algorithms: Towards optimal learning rate policies

Abstract This paper shows how to use results of statistical learning theory and stochastic algorithms to have a better understanding of the convergence of Reinforcement Learning (RL) once it is formulated as a fixed point problem. This can be used to propose improvement of RL learning rates. First, our analysis shows that the classical asymptotic convergence rate is pessimistic and can be replaced by with , and the number of iterations. Second, we propose a dynamic optimal policy for the choice of the learning rate used in RL. We decompose our policy into two interacting levels: the inner and outer levels. In the inner level, we present the PASS algorithm (for “PAst Sign Search”) which, based on a predefined sequence of learning rates, constructs a new sequence for which the error decreases faster. The convergence of PASS is proved and error bounds are established. In the outer level, we propose an optimal methodology for the selection of the predefined sequence. Third, we show empirically that our selection methodology of the learning rate outperforms significantly standard algorithms used in RL for the three following applications: the estimation of a drift, the optimal placement of limit orders, and the optimal execution of a large number of shares.
<i>ScreenPlay</i>: A topic-theory-inspired interactive system

ScreenPlay is a unique interactive computer music system (ICMS) that draws upon various computational styles from within the field of human–computer interaction (HCI) in music, allowing it to transcend the socially contextual boundaries that separate different approaches to ICMS design and implementation, as well as the overarching spheres of experimental/academic and popular electronic musics. A key aspect of ScreenPlay ’s design in achieving this is the novel inclusion of topic theory, which also enables ScreenPlay to bridge a gap spanning both time and genre between Classical/Romantic era music and contemporary electronic music; providing new and creative insights into the subject of topic theory and its potential for reappropriation within the sonic arts.
Concept Timestamping on Blockchain and Decentralization of Patents

We describe a system for registering new inventions and design based on conceptual graphs and the blockchain. Instead of describing the innovation in text as in patent claims, the claims are documented using conceptual graphs as a structured graphical method for capturing concepts and their relationship. Conceptual graphs as a structured graphical method allows easier automatic semantic comparisons. It obviates the need from extracting semantic concepts from text using natural language processing techniques. The graphs then can be hashed and submitted to the blockchain for timestamping to claim the innovation.
Context-Driven Corpus-Based Model for Automatic Text Segmentation and Part of Speech Tagging in Setswana Using OpenNLP Tool

Setswana is an under-resourced Bantu African language that is morphologically rich with the disjunctive writing system. Developing NLP pipeline tools for such a language could be challenging, due to the need to balance the linguistics semantics robustness of the tool with computational parsimony. A Part-of-Speech (POS) tagger is one such NLP tool for assigning lexical categories like noun, verb, pronoun, and so on, to each word in a text corpus. POS tagging is an important task in Natural Language Processing (NLP) applications such as information extraction, Machine Translation, Word prediction, etc. Developing a POS tagger for a morphologically rich language such as Setswana has computational linguistics challenges that could affect the effectiveness of the entire NLP system. This is due to some contextual semantics features of the language, that demand a fine-grained granularity level for the required POS tagset, with the need to balance tool semantic robustness with computational parsimony. In this paper, a context-driven corpus-based model for text segmentation and POS tagging for the language is presented. The tagger is developed using the Apache OpenNLP tool and returns the accuracy of 96.73%.
A Multi-Column Deep Framework for Recognizing Artistic Media

We present a multi-column structured framework for recognizing artistic media from artwork images. We design the column of our framework using a deep neural network. Our key idea is to recognize the distinctive stroke texture of an artistic medium, which plays a key role in distinguishing artistic media. Since stroke texture is in a local scale, the whole image is not proper for recognizing the texture. Therefore, we devise two ideas for our framework: Sampling patches from an input image and employing a Gram matrix to extract the texture. The patches sampled from an input artwork image are processed in the columns of our framework to make local decisions on the patch, and the local decisions from the patches are merged to make a final decision for the input artwork image. Furthermore, we employ a Gram matrix, which is known to effectively capture texture information, to improve the accuracy of recognition. Our framework is trained and tested using two real artwork image datasets: WikiSet of traditional artwork images and YMSet of contemporary artwork images. Finally, we build SynthSet, which is a collection of synthesized artwork images from many computer graphics literature, and propose a guideline for evaluating the synthesized artwork images.
A software tool for FCM aggregation employing credibility weights and learning OWA operators

In this study, we present the functionalities of a new tool for FCMs using credibility weights and OWA-based operators for aggregation tasks. The average aggregation method for weighted interconnections among concepts is the most used method in FCM modeling. The aim of this research work is to (i) propose an alternative aggregation method based on learning OWA operators in aggregating FCM weights, assigned by many experts and/or stakeholders and (ii) to estimate and rank the experts' credibility using a distance-based method. The applicability and usefulness of the proposed methodology in modeling and decision-making is demonstrated using poverty eradication strategies under DAY-NRLM (Deendayal Antyodaya Yojana-National Rural Livelihoods Mission) of India. The results produced by the proposed learning OWA operators are compared with the known average aggregation method of FCMs. These results imply that the proposed alternative FCM aggregation approach is really challenging when a large number of experts and stakeholders are engaged to design the overall FCM model.
Contextual Emotion Detection in Text Using Ensemble Learning

As human beings, it is hard to interpret the presence of emotions such as sadness or disgust in a sentence without the context, and the same ambiguity exists for machines also. Emotion detection from facial expressions and voice modulation easier than emotion detection from text. Contextual emotion detection from text is a challenging problem in text mining. Contextual emotion detection is gaining importance, as people these days are communicating mainly through text messages, to provide emotionally aware responses to the users. This work demonstrates ensemble learning to detect emotions present in a sentence. Ensemble models like Random Forest, Adaboost and Gradient Boosting have been used to detect emotions. Out of the three models, it has been found that Gradient Boosting Classifiers predicts the emotions better than the other two classifiers.
Measuring Routine Variability of Daily Activities with Image Complexity Metrics

Since the abrupt change of daily routines can be an early symptom of cognitive impairment, it is important to measure and track the variability of daily routines of the elderly living alone in terms of their healthcare. This study is motivated by the idea that the degree of image complexity manifested in a person's day-to-day schedule chart is related to the degree of routine variability of his/her daily activities. To test this idea, synthetic data on daily activity logs containing varying degrees of routine variability was created, and a schedule bar chart image was generated based on the synthetic data. Then this study examines whether and to what extent the routine variability inherent in the dataset can be measured by existing image complexity metrics, which have been used to a pattern tendency of an image. The results indicate that the outcomes from three metrics, including Shannon Entropy, GLCM-Entropy, and GLCM-Energy, are well-associated with the degree of routine variability manifested in different aspects of daily activity schedules (i.e., start-time, duration, non-routine-contributing activities, and sequence of routine-contributing activities).
Bokeh

Physical infrastructures that facilitate e.g., delivery of power, water and communication capabilities are of intrinsic importance in our daily lives. Accurate maps of physical infrastructures are important for permitting, maintenance, repair and growth but can be considered a commercial and/or security risk. In this paper, we describe a method for obfuscating physical infrastructure maps that removes sensitive details while preserving key features that are important in commercial and research applications. We employ a three-tiered approach: tier 1 does simple location fuzzing, tier 2 maintains connectivity details but randomizes node/link locations, while at tier 3 only distributional properties of a network are preserved. We implement our tiered approach in a tool called Bokeh which operates on GIS shapefiles that include detailed location information of infrastructure and produces obfuscated maps. We describe a case study that applies Bokeh to a number of Internet Service Provider maps. The case study highlights how each tier removes increasing amounts of detail from maps. We discuss how Bokeh can be generally applied to other physical infrastructures or in local services that are increasingly used for e-marketing.
Research of Methods of Steganographic Protection of Audio Information Based on Video Containers

The chapter describes investigations of steganographic methods of information protection, which use containers represented by files in AVI format. MP3 audio files are selected as data that is embedded in the used container. For the introduction of audio data, the video container was divided into control and information parts. Secret data were embedded in the information part of the video file, which did not lead to distortions when the container file is played. Studies have been conducted to find repetitive blocks in the structure of a video container. The chapter analyzed the determination of the amount of embedded data for containers with different parameters: color saturation, video file resolution, video file length, compression quality, and frames per second. An algorithm has been developed for the introduction and extraction of information represented by sound formats.
A Taxonomy for Selecting Wearable Input Devices for Mixed Reality

Composite wearable computers consist of multiple wearable devices connected together and working as a cohesive whole. These composite wearable computers are promising for augmenting our interaction with the physical, virtual, and mixed play spaces (e.g., mixed reality games). Yet little research has directly addressed how mixed reality system designers can select wearable input devices and how these devices can be assembled together to form a cohesive wearable computer. We present an initial taxonomy of wearable input devices to aid designers in deciding which devices to select and assemble together to support different mixed reality systems. We undertook a grounded theory analysis of 84 different wearable input devices resulting in a design taxonomy for composite wearable computers. The taxonomy consists of two axes: TYPE OF INTERACTIVITY and BODY LOCATION. These axes enable designers to identify which devices fill particular needs in the system development process and how these devices can be assembled together to form a cohesive wearable computer.
Clustering-Based Prototype Generation for Imbalance Classification

Class imbalance classification has become a crucial problem in machine learning. Under-sampling is a widely adopted technique to address imbalance classification, which mainly depends on either randomly or heuristically resampling on the majority class samples. These sample-based under-sampling methods ignore part of the majority class information during the training. In this paper, we propose a clustering-based prototype generation technique to generate representative the majority and minority class instances with relatively balance ratio, which reduces the imbalanced ratio and the overlap of boundary samples, so as to facilitate classification tasks. We evaluate this algorithm on 8 imbalanced datasets, showing that the proposed method outperforms the other three under-sampling approaches.
Boosting learning to rank with user dynamics and continuation methods

Learning to rank (LtR) techniques leverage assessed samples of query-document relevance to learn effective ranking functions able to exploit the noisy signals hidden in the features used to represent queries and documents. In this paper we explore how to enhance the state-of-the-art LambdaMart LtR algorithm by integrating in the training process an explicit knowledge of the underlying user-interaction model and the possibility of targeting different objective functions that can effectively drive the algorithm towards promising areas of the search space. We enrich the iterative process followed by the learning algorithm in two ways: (1) by considering complex query-based user dynamics instead than simply discounting the gain by the rank position; (2) by designing a learning path across different loss functions that can capture different signals in the training data. Our extensive experiments, conducted on publicly available datasets, show that the proposed solution permits to improve various ranking quality measures by statistically significant margins.
Active Dataset Generation for Meta-learning System Quality Improvement

Meta-learning use meta-features to formally describe datasets and find possible dependencies of algorithm performance from them. But there is not enough of various datasets to fill a meta-feature space with acceptable density for future algorithm performance prediction. To solve this problem we can use active learning. But it is required ability to generate nontrivial datasets that can help to improve the quality of the meta-learning system. In this paper we experimentally compare several such approaches based on maximize diversity and Bayesian optimization.
Gaze Tracking Algorithm Based on Projective Mapping Correction and Gaze Point Compensation in Natural Light

Gaze tracking in natural light is a challenging task due to restricted head movement and low accuracy. This paper presents a novel gaze tracking algorithm based on projective mapping correction and fixation point compensation to address this problem. Proposed algorithm utilizes the vector between iris center and eye corners as binocular tracking features to estimation the point of gaze, however, the features of iris center and eye corner are very vulnerable to head movements. We analyze the positional relationship of eye features on image while head moving, and propose a method based on projective mapping correction to adjust the feature vector to reduce the impact of head movement. Then the estimation result is compensated by a support vector regression model which created on the relationship between head movement and point of gaze deviation. The gaze mapping model which can handle unconstrained head movements is constructed. The experiments show that the gaze tracking algorithm proposed can effectively reduce the impact of head movement, and improve the fixation point estimation precision in natural light.
3-D Path Planning Using Subatomic Particles and Feynman Diagrams

This paper offers a 3D path planning algorithm for the navigation of unmanned aerial vehicles, the fusion of photonic mapping techniques and Feynman diagrams to determine an important factor in planning, such as the shortest route, which directly influences the reduction of arrival time; in a designated task avoiding collisions in a semi-structured environment. The planning algorithm is based on integrating elements of photonic presence. The existence or absence of matter is represented by different types of subatomic particles respectively in the virtual environment, the smaller number of links between free particles form nodes and allow to generate a trajectory that is the shortest distance of navigation. This allows to optimize the working times in the different types of UAV this new planning technique due to photonic mapping and particle displacement according to quantum theory can contribute in future research with applications in nanotechnology or in environments of scalable dimensions. Finally, the algorithm is evaluated in 3D virtual environments to verify its correct functioning.
An Effective Differential Evolution With Binary Strategy for Feature Selection Problem

In this paper, an effective differential evolution is proposed with binary strategy to solve feature selection problem. Firstly, a new binary mutation operator and a binary crossover operator are designed. The two-stage adaptive strategy is constructed in the mutation operator to generate new individuals to improve the diversity of the population. Then, the adaptive cross-parameter selection based on individual is developed in the crossover operator to fully exploit each potential optimal individual. Finally, six benchmark data sets are adopted to evaluate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm significantly improves the classification accuracy, reduction rate and time cost.
Pedagogical frameworks and principles for mobile (language) learning to support related teacher education

In this paper results of the EU projects ‘Designing and Evaluating Innovative Mobile Pedagogies’ (DEIMP, http://www.deimpeu.com/) and ‘Mobilising and Transforming Teacher Educators’ Pedagogies’ (MTTEP, http://www.mttep.eu/) are presented. Two key outputs, the Mobile Learning Toolkit and its iPAC framework (MTTEP) and the principles for innovative mobile learning (m-learning, DEIMP), both targeted at stimulating pedagogically sound m-learning practices in teacher education and schools, are highlighted. To enhance these general pedagogical resources for use in professional development activities specifically for language teachers and teacher educators, I refer to some currently available frameworks and guidelines for Mobile Assisted Language Learning (MALL) and teaching.
SpookChain: Chaining a Sponge-Based AEAD with Beyond-Birthday Security

We present $$\mathsf {SpookChain}$$, a new online authenticated encryption (OAE) mode that offers several appealing features:
The development of computer adaptive test and item response theory with 4 parameters based the logistics model

Abstract Students’ abilities can be measured by making a test whose the number and content of questions have been determined for each student. However, it is less effective because the distribution of students’ abilities is different. By using Computer Adaptive Test (CAT) and Item Response Theory (IRT), the test can be done more quickly even though each student might solve different number and content questions. Therefore, in this research we develop CAT and IRT with 4 parameters based logistics model to evaluate the students’ ability. Some experiments conducted on 27 students with 50 number of questions were analyzed and compared with Classical Test Theory (CTT). The results indicate the system can be used as an alternative tool for evaluating the students’ ability.
Trend Analysis of Cyber Security Research Published in HFES Proceedings from 1980 to 2018

The prevalence of information technology (IT) and widespread use of electronic data storage systems by individuals, corporations, and government branches has prompted investigation into threats to cyber security and data privacy. Within the human factors community, researchers have sought to identify issues related to cyber-attacks and cyber defense and human-centered design. These issues range from placing too much trust in modern technology, to remaining vigilant in the presence of cyber threats such as phishing emails. This study aimed to evaluate trends in cyber security research published in the Proceedings of the Human Factors and Ergonomics Society Annual Meeting from 1980 to 2018. This paper reviewed 114 unique articles, identified using keyword terms related to cyber security. Results were organized, analyzed, and graphed based on topic areas, author and funding agency affiliations, and applicable domains. Analyses indicated that cyber security research has grown exponentially in recent years, with nearly 73% of relevant proceedings papers produced since 2010. Gaps in the literature, directions for future research, and emerging issues related to training are also discussed.
AuFloat (Autonomous Float) Based-on Artificial Inteligent and LORA (Long Range) Using Haar Cascade Method for Rescuing of Water Accident Victims

The number of water accidents in Indonesia has increased significantly in the last five years. The Evacuation Team must work quickly to carry out a rescue mission for the victims who nearly drowned. Therefore, this research made Aufloat (Autonomous Float) based on LORA (Long Range) and Artificial Intelligent using image processing with Haar Cascade method as an aid for drowning victims because of water accidents. With this feature, it aims to detect victims and move quickly to victims in evacuation activities. From the results of trial, Aufloat, weighing ± 5 kg and payload 96 kg, has an average speed of 0.94 m/s from 20 trials at different distances, the camera on Aufloat can detect victims well at a maximum distance of 3 to 4 meters and can function well at transmission distances of up to 700 meters.