Reviewerly/exHarmony huggingface dataset:
Response Generation to Out-of-Database Questions for Example-Based Dialogue Systems

Example-based dialogue systems are often used in practice because of their robustness and simple architecture. However, when these systems are given out-of-database questions that are not registered in the question-response database, they have to respond with a fixed backup response, which can make users disengaged in the dialogue. In this study, we address response generation for out-of-database questions to make users perceive that the system understands the question itself. We define question types observed in the speed-dating scenario which is based on open-domain dialogue. Then we define possible response frames for each question type. We propose a sequence-to-sequence model that directly generates an appropriate response frame from an input question sentence in an end-to-end manner. The proposed model also explicitly integrates a question type classification to take into account the question type of the out-of-database question. Experimental results show that integrating the question type classification improved the response generation, and could exactly match 69.2% of response frames provided by human annotators.
Automatic Detection of Breast Cancer in Mammographic Image Using the Histogram Oriented Gradient (HOG) Descriptor and Deep Rule Based (DRB) Classifier Method

Breast cancer is a cell disease in the breast glands that evolve abnormally and become benign or malignant tumors. Breast cancer is a common disease in developing countries and refers to malignancies that can be life-threatening. Mammographic imaging examination is performed to determine the mass of the breast lump to be analyzed for its malignancy. In this research, digital image processing is performed to diagnose breast cancer using mammographic images and feature extraction data with Histogram of Oriented Gradient (HOG), which is further classified using Deep Rule Based (DRB) Classifier. This research was conducted by dividing the data 90% for training data and 10% for testing data. Data are classified into three classes, consisting of normal, benignant, and malignant. The best accuracy obtained is 92.00%.
Comparison of Distance Measurement in Time Series Clustering for Predicting Bitcoin Prices

Since the development of Bitcoin, the first blockchain-based cryptocurrency, many cryptocurrencies have formed and have traded in markets. The integrity and anonymity of cryptocurrency was enough to raise its value and its price gained worldwide attention. Therefore, many studies are being carried out to predict the price of cryptocurrency for make a profit. We cluster time series through K-Medoids algorithm and train and evaluate each cluster with predictive models. We also examine the predictive performance in Bitcoin price according to the various distance measurement of clustering.
Ecological Development Strategy of Coastal Sport Projects of Jiangsu Province

Zheng, W., 2020. Ecological development strategy of coastal sport projects of Jiangsu Province. In: Wan, Z. (ed.), Coordinated Sustainable Development in Coastal Areas: Environment and Economy. Journal of Coastal Research, Special Issue No. 109, pp. 152–158. Coconut Creek (Florida), ISSN 0749-0208.Jiangsu is located in the Yangtze River Delta and many cities represented by Lianyungang and Yangzhou have coastal resources. Therefore, the coastal area of Jiangsu has unique ecological environment conditions, and its future development potential is huge. In order to promote a sustainable future, through the investigation of the ecological development of coastal sports, this paper finds that the current environmental protection status of coastal sports tourism is not optimistic by means of questionnaire survey and expert interview. This is mainly reflected in the low environmental awareness of employees and tourists, and the insufficient environmental protection of facilities in sports. Using PEST model, it can be found that ecological sports projects in Jiangsu coastal area need to be promoted at the administration level, with emphasis on increasing investment in technological innovation and environmental protection training.
Learning to Profile

Meta-learning approaches have shown great success in solving challenging knowledge transfer and fast adaptation problems with few samples in vision and language domains. However, few studies discuss the practice of meta-learning for large-scale industrial applications, e.g., representation learning for e-commerce platform users. Although e-commerce companies have spent many efforts on learning accurate and expressive representations to provide a better user experience, we argue that such efforts cannot be stopped at this step. In addition to learning a strong profile of user behaviors, the challenging question about how to effectively transfer the learned representation and quickly adapt the learning process to the subsequent learning tasks or applications is raised simultaneously.
Anti-unification and the theory of semirings

It was recently shown that anti-unification over an equational theory consisting of only unit equations (more than one) is nullary. Such pure theories are artificial and are of little effect on practical aspects of anti-unification. In this work, we extend these nullarity results to the theory of semirings, a heavily studied theory with many practical applications. Furthermore, our argument holds over semirings with commutative multiplication and/or idempotent addition. We also cover a few open questions discussed in previous work.
How to Complement Learning Analytics with Smartwatches?

To obtain a holistic perspective on learning, a multimodal technical infrastructure for Learning Analytics (LA) can be beneficial. Recent studies have investigated various aspects of technical LA infrastructure. However, it has not yet been explored how LA indicators can be complemented with Smartwatch sensor data to detect physical activity and the environmental context. Sensor data, such as the accelerometer, are often used in related work to infer a specific behavior and environmental context, thus triggering interventions on a just-in-time basis. In this dissertation project, we plan to use Smartwatch sensor data to explore further indicators for learning from blended learning sessions conducted in-the-wild, e.g., at home. Such indicators could be used within learning sessions to suggest breaks, or afterward to support learners in reflection processes.
PAxSPL

In this extended abstract, we discuss the Journal First summary of our work published in the Journal of Software: Practice and Experience (SPE) [1].
Recognizing the Waving Gesture in the Interaction with a Social Robot

Humans use a wide range of non-verbal social signals while communicating with each other. Gestures are part of these signals and social robots should be able to recognize them for responding appropriately during a dialogue and being more socially believable. Gesture recognition is a hot topic in Computer Vision since a long time. This is particularly due to the fact that the segmentation of foreground objects from a cluttered background is a challenging problem, especially if it has to be performed in real-time. In this paper, we propose a vision-based framework for making social robots capable of recognizing and responding in real-time to a specific greeting gesture, namely the hand waving. The framework is based on a Convolutional Neural Network model trained to recognize hand gestures. Preliminary experiments in a lab setup with the social robot Pepper indicate that the robot correctly recognizes the wave gesture 90% of the times and answers appropriately in real-time by waving itself, thus increasing its social believability.
How Can a Robot Trigger Human Backchanneling?

In human communication, backchanneling is an important part of the natural interaction protocol. The purpose is to signify the listener's attention, understanding, agreement, or to indicate that a speaker should go on talking. While the effects of backchanneling robots on humans have been investigated, studies of how and when humans backchannel to talking robots is poorly studied. In this paper we investigate how the robot's behavior as a speaker affects a human listener's backchanneling behavior. This is interesting in Human-Robot Interaction since backchanneling between humans has been shown to support more fluid interactions, and human-robot interaction would therefore benefit from mimicking this human communication feature. The results show that backchanneling increases when the robot exhibits backchannel-inviting cues such as pauses and gestures. Furthermore, clear differences between how a human backchannels to another human and to a robot are shown.
Improving Approximate Logic Neuron Model by Means of a Novel Learning Algorithm

Inspired by the dynamic dendritic architecture of biological neurons, the approximate logic neuron model (ALNM) is a novel model recently proposed by us. ALNM owns four layers, namely, the synaptic layer, the dendritic layer, the membrane layer, and the cell body. Through neural pruning function, the model can omit useless synapses and unnecessary branches of dendrites after the training process. In other words, it will form a unique and simplified dendritic structure for each particular classification task. Further, the simplified dendritic structure can be completely substituted by logic circuits, which makes ALNM be capable of running in hardware. However, although it has satisfactory performances to solve classification problems, it still suffers from some disadvantages owing to its learning algorithm, named batch gradient descent (BGD) algorithm. It is because that, using all the training data for each iteration is time-consuming and it is unsuitable for large scale problems. In addition, BGD cannot adaptively adjust the learning rate during the whole training process, which will converge slowly in the neighborhood of saddle points, and oscillate in the steep region of gradient space. To settle the above issues, we propose a novel stochastic adaptive gradient descent (SAGD) algorithm, which uses stochastic gradient descent information and adaptively adjusts the learning rate, to improve the classification performances of ALNM. In our experiments, ALNM trained by the new algorithm is evaluated on three benchmark classification datasets, and experimental results demonstrate that it performs significantly better than the original model in terms of accuracy and convergence rate.
A Cluster-Based Under-Sampling Algorithm for Class-Imbalanced Data

The resampling methods are among the most popular strategies to face the class imbalance problem. The objective of these methods is to compensate the imbalanced class distribution by over-sampling the minority class and/or under-sampling the majority class. In this paper, a new under-sampling method based on the DBSCAN clustering algorithm is introduced. The main idea is to remove the majority class instances that are identified as noise by DBSCAN. The proposed method is empirically compared to well-known state-of-the-art under-sampling algorithms over 25 benchmarking databases and the experimental results demonstrate the effectiveness of the new method in terms of sensitivity, specificity, and geometric mean of individual accuracies.
Disseminating Soil Alterations Using Geoportal on Natural Phenomena: The Case of Cotopaxi-Ecuador Volcano

The Cotopaxi Volcano is one of the most active stratovolcanoes in the world. It is located in the center of Ecuador, surrounded by densely populated cities as Latacunga, Machachi, or Sangoqui, in which the agricultural sector is very important for economic development. Ash emissions and phreatomagmatic eruptions characterized the eruptive process of Cotopaxi volcano in august 2015. A mineralogical study of the ashes showed the presence of heavy metals, which could have been transferred to the soil and could affect agricultural and livestock activities. To evaluate the relationship between the concentrations of Cd, Cu, Pb, Hg, Cr, Co, Ni, Zn and as between the soil, ash and the comparison with local regulations, three farms (Altamira farm, San Ramon farm and La Laguna farm) in the Canton of Mejia were defined as the study area. In those locations, the soil samples were georeferenced with a handheld GPS. The analysis was carried out before acid digestion (EPA 3050B1 method) in an ICP-OES and AMA 254 atomic absorption spectrophotometer. The metals analyzed were kept within limits defined in TULSMA Book VI: Annex 2 Soil Resource local regulations. In order to improve the visibility of the study, the presentation of the results was carried out in an open-access geoportal implemented in proprietary software. This geoportal allows the dynamic and interactive visualization of the different concentrations of heavy metals in their corresponding location.
A New Adaptive Online Learning using Computational Intelligence

This study aimed to develop an online learning system that was adaptive to students who wished to learn electrical machine modules based on their abilities. Adaptive use of online learning functioned to determine the category of students' ability to access modules in online learning. Online learning was also able to provide the determination of modules which can then be done by students, so students can learn independently. Adaptive capabilities in online learning were implemented by utilizing computational intelligence algorithms, namely Naive Bayes and Bayes Network. Naive Bayes was tasked with processing students `pre-test data in adaptive online learning for the classification of students' abilities so that after the results of the pretest appeared, students will be given modules that matched their abilities. Whereas Bayes Network used to process student post-test data after students worked on the modules that have been given, adaptive online learning provided the next module to work according to the abilities and desires of students. The testing results of the use of Naive Bayes and Bayes Network on Adaptive Online Learning obtained an average accuracy of 85%.
Association Rule Mining for Unknown Video Games

Computational intelligence agents can reach expert levels in many known games, such as Chess, Go, and Morris. Those systems incorporate powerful machine learning algorithms, which are able to learn from, e.g., observations, play-traces, or by reinforcement learning. While many black box systems, such as deep neural networks, are able to achieve high performance in a wide range of applications, they generally lack interpretability. Additionally, previous systems often focused on a single or a small set of games, which makes it a cumbersome task to rebuild and retrain the agent for each possible application. This paper proposes a method, which extracts an interpretable set of game rules for previously unknown games. Frequent pattern mining is used to find common observation patterns in the game environment. Finally, game rules as well as winning-/losing-conditions are extracted via association rule analysis. Our evaluation shows that a wide range of game rules can be successfully extracted from previously unknown games. We further highlight how the application of fuzzy methods can advance our efforts in generating explainable artifical intelligence (AI) agents.
Judicial Dialogue on Data Retention Laws in Europe in the Digital Age: Concluding Remarks

The chapter is a summary of the analysis contained in chapters of the book regarding European constitutional courts’ approach to data retention law. It presents an outline of judicial dialogue in this matter. In addition, the constitutional standard concerning both the data retention mechanism itself and the premises for providing access to retained data are indicated.
Modeling Global Body Configurations in American Sign Language

American Sign Language (ASL) is the fourth most commonly used language in the United States and is the language most commonly used by Deaf people in the United States and the English-speaking regions of Canada. Unfortunately, until recently, ASL received little research. This is due, in part, to its delayed recognition as a language until William C. Stokoe's publication in 1960. Limited data has been a long-standing obstacle to ASL research and computational modeling. The lack of large-scale datasets has prohibited many modern machine-learning techniques, such as Neural Machine Translation, from being applied to ASL. In addition, the modality required to capture sign language (i.e. video) is complex in natural settings (as one must deal with background noise, motion blur, and the curse of dimensionality). Finally, when compared with spoken languages, such as English, there has been limited research conducted into the linguistics of ASL. We realize a simplified version of Liddell and Johnson's Movement-Hold (MH) Model using a Probabilistic Graphical Model (PGM). We trained our model on ASLing, a dataset collected from three fluent ASL signers. We evaluate our PGM against other models to determine its ability to model ASL. Finally, we interpret various aspects of the PGM and draw conclusions about ASL phonetics. The main contributions of this paper are
What’s in a Caption?: Leveraging Caption Pattern for Predicting the Popularity of Social Media Posts

In the past few years, social media has become an integral part of modern society. It also has surfaced as an influential tool that helps a business or individual in gaining identity and reputation. Predicting the popularity of images before they are posted on social media thus may have a profound impact to reveal individual preference and public attention. However, an accurate prediction is a challenging task, mainly on account of factors that play a part in this. Previous studies, although achieve favourable results, overlook one unique characteristic of semantics in textual metadata, i.e., the language modeling, to better model the context information of a post. To that end, we propose to exploit the language modeling features together with user profile and post metadata features. The language model features are extracted by utilizing the probability of word occurrence, while the user profile and post metadata features are provided as attributes by the original data source. Several state-of-the-art statistical modeling techniques are employed to investigate the performance of the proposed features on different estimation procedures. Experiments on a large-scale Flickr dataset demonstrate the benefits of the proposed features on predicting the popularity of social media posts.
EUROVOLC tool for citizen science observations of volcanic phenomena

&amp;lt;p&amp;gt;One of the aims of EUROVOLC is to raise awareness and share data by exploiting existing tools for hazard and risk. Here we present the ongoing effort within EUROVOLC WP12 to create an online tool to collect information from people witnessing volcanic events at European or other volcanoes.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In the recent past, building on the experience from earthquakes, and from the trans-national effects of Eyjafallaj&amp;amp;#246;kull eruption, European research groups have built tools (e.g. questionnaires or apps) for facilitating the collection of data by citizens. These efforts are presently fragmented and sparse across Europe (and across the world).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;As the first step we have conducted a reconnaissance survey of existing citizen science tools in volcanology (from operational and research projects), available for download through EUROVOLC website&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;One of the aims of EUROVOLC is to raise awareness and share data by exploiting existing tools for hazard and risk. Here we present the ongoing effort within EUROVOLC WP12 to create an online tool to collect information from people witnessing volcanic events at European or other volcanoes.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In the recent past, building on the experience from earthquakes, and from the trans-national effects of Eyjafallaj&amp;amp;#246;kull eruption, European research groups have built tools (e.g. questionnaires or apps) for facilitating the collection of data by citizens. These efforts are presently fragmented and sparse across Europe (and across the world).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;As the first step we have conducted a reconnaissance survey of existing citizen science tools in volcanology (from operational and research projects), available for download through EUROVOLC website.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The new EUROVOLC tool will:&amp;lt;br&amp;gt;- access and collate data collected by several pre-existing tools. These tools currently include &amp;amp;#8216;myVolcano&amp;amp;#8217; by British Geological Survey; sulphur dioxide and ash recording tools by Iceland Met Office; Osservatorio Vesuviano web questionnaire &amp;amp; Tefranet by INGV-Catania. These tools were selected based on whether their data can be &amp;amp;#8216;pulled&amp;amp;#8217; in real-time;&amp;lt;br&amp;gt;- allow additional tools to be incorporated as they become available;&amp;lt;br&amp;gt;- allow recording of new data by the users;&amp;lt;br&amp;gt;- allow visualizing on a map the data in which the users are interested in, that can be selected by region/country, by recording time, or by observed phenomenon;&amp;lt;br&amp;gt;- allow downloading the data in which the users are interested in&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this way, the users of EUROVOLC tool will have access to observations collected by the multiple tools available across EUROPE through a single access point.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The EUROVOLC tool will become available in July 2020.&amp;lt;/p&amp;gt;
Construction of Signal Sets From Quotient Rings of the Quaternion Orders Associated With Arithmetic Fuchsian Groups

This paper aims to construct signal sets from quotient rings of the quaternion over a real number field associated with the arithmetic Fuchsian group Γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4g</sub> , where g is the genus of the associated surface. These Fuchsian groups consist of the edge-pairing isometries of the regular hyperbolic polygons (fundamental region) P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4g</sub> , which tessellate the hyperbolic plane D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The corresponding tessellations are the self-dual tessellations {4g, 4g}. Knowing the generators of the quaternion orders which realize the edge-pairings of the polygons, the signal points of the signal sets derived from the quotient rings of the quaternion orders are determined. It is shown by examples the relevance of adequately selecting the ideal in the maximal order to construct the signal sets satisfying the property of geometrical uniformity. The labeling of such signals is realized by using the mapping by set partitioning concept to solve the corresponding Diophantine equations (extreme quadratic forms). Trellis coded modulation and multilevel codes whose signal sets are derived from quotient rings of quaternion orders are considered possible applications.