Reviewerly/exHarmony huggingface dataset:
Relative evolutionary hierarchical analysis for gene expression data classification

Relative Expression Analysis (RXA) focuses on finding interactions among a small group of genes and studies the relative ordering of their expression rather than their raw values. Algorithms based on that idea play an important role in biomarker discovery and gene expression data classification. We propose a new evolutionary approach and a paradigm shift for RXA applications in data mining as we redefine the inter-gene relations using the concept of a cluster of co-expressed genes. The global hierarchical classification allows finding various sub-groups of genes, unifies the main variants of RXA algorithms and explores a much larger solution space compared to current solutions based on exhaustive search. Finally, the multi-objective fitness function, which includes accuracy, discriminative power of genes and clusters consistency, as well as specialized variants of genetic operators improve evolutionary convergence and reduce model underfitting. Importantly, patterns in predictive structures are kept comprehensible and may have direct applicability. Experiments carried out on 8 cancer-related gene expression datasets show that the proposed approach allows finding interesting patterns and significantly improves the accuracy of predictions.
Paper Pocket Pets - Making Wearable Interactive Toys

We present Paper Pocket Pets, an approach to create personal interactive toys that children can carry along with them. Creating Paper Pocket Pets involves four activities that combine traditional crafts with digital fabrication: paper folding to create a pet; fabrication of the pet's shelter (a platform where the pet lives on); fabrication of the pet's electric components; and programming the pet's behavior. We invite the FabLearn audience to create their own pets and explore these activities during the demo at the conference.
Gait Pattern Recognition Using a Smartwatch Assisting Postoperative Physiotherapy

Postoperative rehabilitation is led by physiotherapists and is a vital program that re-establishes joint motion and strengthens the muscles around the joint after an orthopedic surgery. Modern smart devices have affected every aspect of human life. Newly developed technologies have disrupted the way various industries operate, including the healthcare one. Extensive research has been carried out on how smartphone inertial sensors can be used for activity recognition. However, there are very few studies on systems that monitor patients and detect different gait patterns in order to assist the work of physiotherapists during the said rehabilitation phase, even outside the time-limited physiotherapy sessions. In this paper, we are presenting a gait recognition system that was developed to detect different gait patterns. The proposed system was trained, tested and validated with data of people who have undergone lower body orthopedic surgery, recorded by Hirslanden Clinique La Colline, an orthopedic clinic in Geneva, Switzerland. Nine different gait classes were labeled by professional physiotherapists. After extracting both time and frequency domain features from the time series data, several machine learning models were tested including a fully connected neural network. Raw time series data were also fed into a convolutional neural network.
Classification of Real 3D and Fake 3D Video

The tremendous growth in communication and media technology and the wide availability of cheaper end using devices have made 3D video communication very popular due to its immersive experience. It has been observed that a 3D video can be produced either by direct accusation using a 3D camera (say real 3D video) or by rendering from a set of 2D images (say fake 3D video). There are several occasions where it is required to distinguish between such real and fake 3D video sequences. In this paper, an algorithm is proposed which can distinguish the real 3D video from the fake one. A set of distinguishing features has been identified which are primarily based on the vertical parallax and sharpness peculiarities of object edges due to 3D acquisition process and rendering. Finally, two different supervised learning classifiers (Support Vector Machines and Linear discriminant analysis), are being trained using these features to detect the fake 3D video sequences. A comprehensive set of experiments has been carried out to justify the applicability of the proposed detection scheme over the recent existing scheme.
Gamification Design of Shared Bicycle System Based on Fogg Behavior Model

As environmental issues become more prominent, people have focused on green travelling widely. Due to the convenience, environmental protection and low cost properties of sharing bicycles, it has gradually become the main green travelling way in Chines major cities. To improve user participation in shared bicycle system, our study, based on Fogg Behavior Model, proposes a design concept of a small shared bicycle power supply system which raises electricity generated by riding bicycle. The electricity raised in sharing bicycle can also be used to charge the user’s mobile devices. In addition, through the gamification design of user’s mobile interface, it inspires the interaction between users, as well as improves communication between users and systems, users and users. The results from the participants’ feedback are promising, and the lessons learnt and approaches discussed in this paper provide a reference for the study of improving user participation.
Real Time Content-Based News Recommendation in Turkish with Apache Spark

The readers’ behavior have partially turned into consuming news online, after news providers are transformed the printed newspapers into digital web sites. Engaging attention of the readers by retaining them on the web site contents as much as possible to consume more articles has become crucial and as a scarce resource, it is also challenging for the providers. While readers click through on the pages, system records each click for classical counting approach, also measure the time spent on pages which is critical for news providers in digital web sites. In this regard, recommendation engines which builds on the natural language processing with machine learning algorithms play important role to gain insights from the vectorized contents and provide similar articles to the readers. We motivate to analyze news content similarities expanding with tags and emotion similarities by comparing various text analytics methods results starting from sports and travel categories. By adding domain knowledge on the findings, our aim is to have a specific category based approach which could be applied on different news providers contents in Turkey.
Application of Data Augmentation for Accurate Attractiveness Estimation for Food Photography

This research aims to develop a data augmentation framework in order to improve the attractiveness estimation accuracy for food photography. Machine learning-based methods require numerous food images accompanied with their attractiveness for learning the relationship between image features and the attractiveness. To efficiently obtain such food images, we apply data augmentation; the proposed method applies four kinds of image transformations: rotation, scaling, shifting, and random noise addition to the original images accompanied with their attractiveness. The key idea here is to apply the image transformations within a parameter space in which the attractiveness of the transformed image can be regarded as the same as that of the original one. By this way, we can obtain a large number of images accompanied with their attractiveness without any additional subjective experiments. Experimental results showed the effectiveness of the proposed method framework.
Tacit Knowledge in Software Evolution

This chapter describes two perspectives on the identification and externalisation of tacit knowledge, that is expertise that is difficult to verbalise, within long-living and continuously evolving systems. During the design time of a software system, heuristics and machine learning classifiers can be used to identify and externalise tacit knowledge. For instance, externalised tacit security knowledge supports requirement engineers to understand security-related requirements. During the run time of a software system, tacit knowledge about a system’s usability can be captured through monitoring user interactions. The identification and extraction of tacit usage knowledge can improve usability-related aspects and even trigger new functional requirement requests.
K-aggregated transformation of discrete distributions improves modeling count data with excess ones

The excess one pattern in count data has been documented in ecology but it has not been explicitly modeled or examined. In this study, we introduce a k-aggregated transformation of discrete distributions to better model count data with excess ones in a Bayesian generalized linear model framework and demonstrate its use with two groups of case studies (group 1: seabird bycatch in longline fisheries and Legionnaires disease incidence; group 2: survey abundance of Leadbeater’s possum and Frigatebird nesting sites). Group 1 examples have a clear excess one data pattern, and these examples are used to demonstrate the concept of the k-aggregation technique. On the other hand, group 2 examples lack a clear excess one pattern, and a modeler may not be motivated enough to use the k-aggregation technique in these cases. Nonetheless, k-aggregated transformation demonstrated better performance for both groups of examples. In all our case studies, the excess zero pattern co-occurred with an excess one pattern, and the excess zeros were modeled thorough either a zero-inflated or hurdle configuration. The better performance of k-aggregated distributions is due to their flexibility of adapting to the relatively high frequency of singletons in the data sets. This new technique has broad applicability and utility in improving modeling count data with potential excess ones.
Online Neural Cell Tracking Using Blob-Seed Segmentation and Optical Flow

Existing neural cell tracking methods generally use the morphology cell features for data association. However, these features are limited to the quality of cell segmentation and are prone to errors for mitosis determination. To overcome these issues, in this work we propose an online multi-object tracking method that leverages both cell appearance and motion features for data association. In particular, we propose a supervised blob-seed network (BSNet) to predict the cell appearance features and an unsupervised optical flow network (UnFlowNet) for capturing the cell motions. The data association is then solved using the Hungarian algorithm. Experimental evaluation shows that our approach achieves better performance than existing neural cell tracking methods.
Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion

Handwriting based gender identification at the word level is challenging due to free style writing, use of different scripts, and inadequate information. This paper presents a new method based on Multi-Gabor Response (MGR) fusion for gender identification at the word level. It first explores weighted-gradient features for word segmentation from text line images. For each word, the proposed method obtains eight Gabor response images. Then it performs sliding window operation over MGR images to smooth the values. For each smoothed MGR images, we perform fusion operation that chooses the Gabor response value which contributes to the highest peak in the histogram. This process results in a feature matrix, which is fed to CNN for gender identification. Experimental results on our dataset (multi scripts) apart from English, and benchmark databases, namely, IAM, KHATT, and QUWI, which contain handwritten English and Arabic text, show that the proposed method outperforms the existing methods.
Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks

The literature concerning the supervised learning algorithms in data stream mining is dominated mainly by pattern classification methods. Only few of them deal with a non-stationary regression. Most of them rely on the Gaussian or Markov models, extend Support Vector Machine or Extreme Learning Machine to regression problems, implement regression trees or polynomial regression for working in a non-stationary environment. We will briefly describe these approaches.
EastMedAtlas: online data access system

The work is dedicated to the development of a regional online free GIS data access system in the Eastern Mediterranean sea. EastMedAtlas consists from the following main parts: User interface (UI), Server and Map service. The user interface provides the database's request and visualize the results on the Map, and plots the data for each requested parameter. The UI support data in ODV and netCDF formats. The EastMedAtlas operates at the base of Apache server software and includes Database (DB), php scripts for providing data exchange between the UI and the Map service at the base of Mapserver software for visualization of geo-spatial data. The EastMedAtlas DB is developed at the base of MySQL and includes free access data of the Eastern Mediterranean sea downloaded through European and international portals, such as for example the SeaDataNet, EMODNET, CMEMS, WDC and other relevant databases. The EastMedAtlas data has received a further Quality Control using the regional QC criteria adopted in SeaDataNet. At present, the EastMedAtlas DB includes metadata and data from more than 20 thousands stations in the Eastern Mediterranean sea. The EastMedAtlas online GIS system is more user friendly and less cumbersome than other oceanographic global and regional online DBs.
A Framework for Knowledge Models Transformation: A Step Towards Knowledge Integration and Warehousing

An intelligent decision support system should based on a knowledge warehouse (KW). A KW gathers knowledge initially expressed in different formalisms and therefore heterogeneous. Consequently, the KW building process requires knowledge homogenisation. This paper deals with this main issue; it introduces a three-layer architecture for a KW; more precisely, it focuses on the first layer architecture called Knowledge Acquisition and Transformation. This layer aims to transform heterogeneous knowledge models into the MOT (Modeling with Object Types) semi-formal language [Paquette, G (2002). Knowledge and Skills Modeling: A Graphical Language for Designing and Learning. Sainte-Foy: University of Quebec Press (in French).] that we have selected as a pivot knowledge model. For this transformation step, first, we design four meta-models; one for MOT and one for each of the three explicit knowledge models, namely, decision tree, association rules and clustering. Secondly, we define 15 transformation rules that we formalise in ATL (Atlas Transformation Language). Finally, we exemplify the knowledge transformation in order to show its usefulness for the KW building process.
Evolutionary Design of Approximate Sequential Circuits at RTL Using Particle Swarm Optimization

Evolutionary circuit design has the ability to explore a wide part of the design space and can lead to satisfactory circuits without human experience and knowledge. In this work, we use Multi-Objective Particle Swarm Optimization to evolve approximate sequential circuits at Register-Transfer Level. A circuit is represented by a two-dimensional array. We aim to produce functional approximate circuits having good trade-off between accuracy, delay and area. The results show the efficiency of the proposed approach.
Ontology-based Thought Organization Support System to Prompt Readiness of Intention Sharing and Its Long-term Practice

To achieve fruitful, creative discussions, it is important for speakers to verbalize and share the intentions behind their utterances with their listeners. Our research objective is to propose an activity support system for cultivating novice researchers’ intention sharing skills. We develop a research activity support system in which researchers can focus on organizing the structure of their thought processes in a pyramid made of chains of inquiries and answers. Then, we explain the support functions, evaluate the system, and explain the results. After learners used the system for about one year, we found that they experienced positive changes in their critical thinking and intention sharing skills. In addition, we found indications that the system was especially useful as a set of “training wheels” for relatively novice researchers.
Dynamic Adaptation for WPANs Collision Prevention in eHealth Environments

This paper presents several adaptive mechanisms to dynamically update the wireless personal area networks (WPANs) parameters that are essential in wearable devices, especially in healthcare environments. Instead of collision detection and recovery, collision prevention is achieved using the proposed algorithms to guarantee a collision-free environment. We present a binary integer programming model for the optimal solution. To avoid the increased complexity, we introduce six suboptimal algorithms. The presented algorithms try to minimize network disruption by updating the parameters of a minimal number of the WPANs. The simulation results show how the algorithms trade off minimal disruption and time complexity.
Investigation and Analysis of College Students' Prevention of Network Fraud

With the increasing frequency of college students surfing from Internet, the problem of network fraud has been increasingly concerned by the society. Through questionnaire survey, it is found that the online fraud information encountered by college students mainly includes fake friends borrowing money; virtual winning fraud information; false website fraud. The reasons are mainly lack of protection awareness, Lack of network supervision and tolerance for fraud. To this end, it is necessary to take comprehensive measures from three aspects: individual, college and society.
PCA Based Guided Bilateral Filter for Medical Color Images

This paper presents a fast and accurate approximation of the bilateral filter for color images such as medical color images. The bilateral filter has a problem of high computational complexity. Various acceleration methods have been proposed in the past. However, most of the existing methods have been developed for grayscale images, not for color images. Therefore, the computational complexity is high even though we apply the state-of-the-art fast approximate methods to color images. This paper tackles the problem based on the idea of the cross/joint bilateral filter. It filters a target image using an additional guide image for higher-quality output. Our key idea is to generate the guide image by the principal component analysis for the color vector distribution of the target image. As a result, the proposed method achieves highly-accurate approximation of the bilateral filter and succeeds in drastically reducing the computational complexity. Our experiments show that the proposed method is robust in high contrast medical color images. Furthermore, we combined the state-of-the-art constant-time bilateral filter with the proposed method. This combination achieved high quality performance in terms of both approximate accuracy and computational complexity.
Chinese Pinyin Input Method in Smartphone Era: A Literature Review Study

This study conducted a literature review on the latest academic publications concerning smartphone-based Pinyin input method. The objective is to explore what academic endeavors have been made to address the key concerns of today’s Pinyin input method. Results show that much work has been concentrated in advancing Pinyin-to-character conversion, by forging a more powerful algorithm more capable of tolerating, detecting and correcting error. Besides, a serial of interface layout optimization attempts have been tried. Based on the analysis of the review result, comments of the existing work and suggestions for future research direct are proposed.
Evaluation of a Model-driven Knowledge Storage and Retrieval IDE for Interactive HRI Systems

Efficient storage and querying of long-term human–robot interaction data requires application developers to have an in-depth understanding of the involved domains. Creating syntactically and semantically correct queries in the development process is an error prone task which can immensely impact the interaction experience of humans with robots and artificial agents. To address this issue, we present and evaluate a model-driven software development approach to create a long-term storage system to be used in highly interactive HRI scenarios. We created multiple domain-specific languages that allow us to model the domain and seamlessly embed its concepts into a query language. Along with corresponding model-to-model and model-to-text transformations, we generate a fully integrated workbench facilitating data storage and retrieval. It supports developers in the query design process and allows in-tool query execution without the need to have prior in-depth knowledge of the domain. We evaluated our work in an extensive user study and can show that the generated tool yields multiple advantages compared to the usual query design approach.
Virtually Teaching Ethics

The CAVE experience is an immersive virtual reality (IVR) environment employing high-resolution, 3D video and audio technology. Using the CAVE, researchers at University College London designed an IVR scenario intended to echo the logical structure of a traditional ‘trolley scenario’ problem, and deployed this activity within an undergraduate Law and Ethics Course. In this chapter we explore how the use of virtual reality can offer students an unparalleled opportunity to reflect on the dissonance between the behaviour they adopt when faced with an ethical dilemma, and the theoretical stance they propose during class discussion. We explore how this personalisation gives rise to sustained student engagement borne out of a desire to understand the discrepancy between principle and practice. Our chapter considers the potential of IVR technology when teaching ethics to future and current professionals. We conclude by considering how such technology can offer more dynamic opportunities for student reflection and how IVR might be sensibly integrated into a broader legal ethics curriculum.
Non-Normal Modal Description Logics

Modal logics are widely used in multi-agent systems to reason about actions, abilities, norms, or epistemic states. Combined with description logic languages, they are also a powerful tool to formalise modal aspects of ontology-based reasoning over an object domain. However, the standard relational semantics for modalities is known to validate principles deemed problematic in agency, deontic, or epistemic applications. To overcome these difficulties, weaker systems of so-called non-normal modal logics, equipped with neighbourhood semantics that generalise the relational one, have been investigated both at the propositional and at the description logic level. We present here a family of non-normal modal description logics, obtained by extending $$\smash {\mathcal {ALC}} $$ -based languages with non-normal modal operators. For formulas interpreted on neighbourhood models over varying domains, we provide a modular framework of terminating, correct, and complete tableau-based satisfiability checking algorithms in $$\textsc {NExpTime}$$ . For a subset of these systems, we also consider a reduction to satisfiability on constant domain relational models. Moreover, we investigate the satisfiability problem in fragments obtained by disallowing the application of modal operators to description logic concepts, providing tight $$\textsc {ExpTime}$$ complexity results.