Reviewerly/exHarmony huggingface dataset:
“Wear and Forget” patch for ambient assisted living

Rising cumulative costs of public hospital stay and its impact on government spending and the economy over the years has necessitated the adoption of other options of administering medical care such as home care, care homes and e-health [1] . The possibility of not having a health care personnel physically present in some of these scenarios created a need for remote patient monitoring. This has impacted positively on the development of new technologies for patient monitoring, personalised care, and assisted living especially as the world population age over the next few decades. These devices include hand-held/pocket-sized activity monitors, wearable patches [2] , wristbands [3] , [4] , watches. While these devices are functional and are potentially cheaper when compared with in-hospital care, they have not been widely adopted for several reasons such as intrusiveness – some of them are rigid and inconvenient to be carried around by patients without substantial change to their everyday lives. Another reason is invasiveness – some devices sample patient blood repeatedly. In this work, we present a non-intrusive (“wear and forget”) patch platform for patient monitoring. This device, fabricated on a flexible polyimide substrate, is light, flexible and conformable to the human body, has a longer range of detection because of integrated flexible battery and supercapacitor, and transfers data collected from patient wirelessly to an RFID reader located nearby, providing opportunity for both ambient and remote monitoring [2] , as well as personalised e-health assistance and continuous monitoring, which enables early detection of declining health [5] .
Critical Thinking Skills: British Parliamentary Debate System to Improve English as Foreign Language (EFL) Students’ Critical Speaking

Debate as one of techniques in teaching speaking has been believed can accommodate and facilitate teachers in improving students speaking skill. It is also believed that students’ critical thinking can be ignited though debate technique. This research, therefore would like to employ the debate technique of British parliamentary system in teaching speaking in order to initiate students critical thinking. This is a quantitative research by applying experimental method. The population of this study was English speaking class in private Higher Education (PTS) in Aceh and the sample were two English speaking classes in Private Higher Education in Aceh. The sample, however, were two English speaking classes in Private Higher Education in Aceh. The data of this study were gained from pre test and post test, thus the results of the test were then analyzed though statistic formula. From the analyzed data, it was showed that in sample 1, the t table was 2.145 and t-test was 6.006, in sample 2 the t table was 2.228 and t-test was 2.936. The research finding revealed that, the t-test found was higher than t-table. It can be concluded that Ho in the study was rejected that there was an improvement on students’ critical thinking skill after applying the British Parliamentary debate system.
Complex Networks Monitoring and Security and Fraud Detection for Enterprises

The purpose of Complex Networks Monitoring and Security and Fraud Detection for Enterprises - CoNeSec track is two-fold: Firstly, the track offers a forum for scientists and engineers to exchange ideas on novel analytical techniques using network log data. Secondly, the track has a thematic focus on emerging technology for complex network, security and privacy. We seek publications on all theoretical and practical work in areas related to the theme above.
APCNN: Tackling Class Imbalance in Relation Extraction through Aggregated Piecewise Convolutional Neural Networks

One of the major difficulties in applying distant supervision to relation extraction is class imbalance, as the distribution of relations appearing in text is heavily skewed. This is particularly damaging for the multi-instance variant of relation extraction. In this work, we introduce a new model called Aggregated Piecewise Convolutional Neural Networks, or APCNN, to address this problem. APCNN relies on the combination of two neural networks, a novel objective function as well as oversampling techniques to tackle class imbalance. We empirically compare APCNN to state-of-the-art approaches and show that it outperforms previous multi-instance approaches on two standard datasets.
Optimization-Based Control for Bearing-Only Target Search With a Mobile Vehicle

This article aims to design an optimization-based controller for a discrete-time Dubins vehicle to approach a target with unknown position as fast as possible by only using bearing measurements. To this end, we propose a bi-objective optimization problem, which jointly considers the performance of estimating the unknown target position and controlling the mobile vehicle to a known position, and then adopt a weighted sum method with normalization to solve it. The controller is given based on the solution of the optimization problem in ties with a least-square estimate of the target position. Moreover, the controller does not need the vehicle's global position information. Finally, the simulation results are included to validate the effectiveness of the proposed controller.
A Novel Aerial Manipulation Design, Modelling and Control for Geometric CoM Compensation

This paper presents the design and modelling of a new Aerial manipulating system, that resolve a displacement of centre of gravity of the whole system with a mechanical device. A prismatic joint between the multirotor and a robotic arm is introduced to make a centre of mass as close as to the geometric centre of the whole system. This paper details also the geometric and dynamic modelling of a coupled system with a Lagrange formalism and control law with a Closed Loop Inverse Kinematic Algorithm (CLIKA). This dynamic inverse control is validated in a Simulink environment showing the efficiency of our approach.
Decentralized Multi-sensor Scheduling for Multi-target Tracking and Identity Management

This paper proposes a multi-target tracking and identity management method with multiple sensors: a primary sensor with a large detection range to provide the targets' state estimates, and multiple secondary sensors capable of recognizing the targets' identities. Each of the secondary sensors is assigned to a sector of the operation area; a secondary sensor decides which target in its assigned sector to be identified and controls itself to identify the target. We formulate the decision-making process as an optimization problem to minimize the uncertainty of the targets' identities subject to the sensor dynamic constraints. The proposed algorithm is decentralized since the secondary sensors only communicate with the primary sensor for the target information, and need not to synchronize with each other. By integrating the proposed algorithm with the existing multi-target tracking algorithms, we develop a closed-loop multi-target tracking and identity management algorithm. The effectiveness of the proposed algorithm is demonstrated with illustrative numerical examples.
Multi-valued Expression Analysis for Collective Checking

Determining if a parallel program behaves as expected on any execution is challenging due to non-deterministic executions. Static analyses help to detect all execution paths that can be executed concurrently by identifying multi-valued expressions, i.e. expressions evaluated differently among processes. This can be used to find collective errors in parallel programs. In this paper, we propose a new method that combines a control-flow analysis with a multi-valued expressions detection to find such errors. We implemented our method in the PARCOACH framework and successfully analyzed parallel applications using MPI, OpenMP, UPC and CUDA.
Evaluation of Manual Alphabets Based Gestures for a User Authentication Method Using s-EMG

At the present time, since mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives, an authentication method that prevents shoulder surfing attacks comes to be important. We are investigating a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, can be detected over the skin surface, and muscle movement can be differentiated by analyzing the s-EMG signals. Taking advantage of the characteristics, we proposed a method that uses a list of gestures as a password in the previous study. In order to realize this method, we have to prepare a sufficient number of gestures that are used to compose passwords. In this paper, we adopted fingerspelling as candidates of such gestures. We measured s-EMG signals of manual kana of The Japanese Sign Language syllabary and evaluated their potential as the important element of the user authentication method.
BioGen: Automated Biography Generation

A biography of a person is the detailed description of several life events including his education, work, relationships and death. Wikipedia, the free web-based encyclopedia, consists of millions of manually curated biographies of eminent politicians, film and sports personalities, etc. However, manual curation efforts, even though efficient, suffers from significant delays. In this work, we propose an automatic biography generation framework BioGen. BioGen generates a short collection of biographical sentences clustered into multiple events of life. Evaluation results show that biographies generated by BioGen are significantly closer to manually written biographies in Wikipedia. A working model of this framework is available at nlpbiogen.herokuapp.com/home/
A Dynamic Ensemble Selection Framework Using Dynamic Weighting Approach

In Dynamic Classifier Selection (DCS) techniques, test sample is classified only by the most competent classifiers. Hence, the major problem in DCS is to find the measures by which competence of classifiers in a pool can be calculated to find out the most competent classifiers. To tackle these issues, we suggest a Framework for Dynamic Ensemble Selection (DES) that uses more than one criterion to calculate the base classifier’s competence level. The framework has three major steps. In first step, training data is used to create a pool consisting of different classifiers. In second step meta-classifier training is performed by extracting meta-features from training data. In third step meta-classifier uses meta-features extracted from test sample to perform an ensemble selection and to predict the final output. In this paper, we suggest some improvements in second step (training) and last step (generalization) of the framework. In training phase, four different models are used as meta-classifiers. While in generalization phase, dynamic weighting scheme is used where meta-classifiers will dynamically assign weights to selected competent classifiers based on their competence level and final decision will be aggregated using a weighting voting scheme. The modifications purposed in this paper altogether enhance performance and accuracy of the framework in contrast with other dynamic selection techniques in literature.
Towards Optimizing Place Experience Using Design Science Research and Augmented Reality Gamification

Studies suggest that augmented reality and game mechanics can lead to increase sense of place. This is important as sense of place provides many benefits ranging from personal place significance, to increase interest and stewardship at the place. These benefits combined can lead to enhancement of an offered service. Therefore, the question asked in this research is how to effectively design an experience using these emerging technologies and optimize for successful outcomes? In this study we outline the design and development process of a pervasive mobile application solution using design science research methodology guidelines to answer this question. Specifically, the application solution replicates a human guide and narration experience in the exhibition of indigenous artworks in a university place by integrating augmented reality, micro location, audio and enhancement through gamification service to increase engagement and experience value. We present observation result data from the first iteration of the design science research methodology by analyzing qualitative usability testing of the application by expert stakeholders. The results indicate that the performance usability of the app is satisfactory, and it provides solid base for the next iteration of the development process.
Distributed Context Petri Nets

Dynamically adaptive software systems are inherently distributed. These systems enable the dynamic adaptation of systems' behavior according to information gathered from their environment, through sensors. As the system behavior is associated with different sensing devices, the complete system is not contained in a single device, but the complete system behavior can come from cooperation and communication between multiple nodes, as the environment evolves. However, existing context-oriented programming languages underlying such systems work under a closed world assumption, in which the system is completely contained and managed in a single node. This assumption hinders the applicability of dynamic adaptations in environments as those proposed in cyber physical systems. To develop the full potential of dynamically adaptive systems, we propose a run-time execution model, distributed context Petri nets, to manage the interactions of behavioral adaptations between remote nodes. Each node consists of a context Petri net extended with the capability to remotely interact with context Petri nets in other nodes. To validate the ability of our model to manage behavioral adaptations in distributed settings, we demonstrate the semantics of the different context dependency relations in face of different situations yielding inconsistencies.
Cloud Capacity Planning Based on Simulation and Genetic Algorithms

Reducing spending on information technology is one important area that enable enterprises to reduce cost. One area where this can be done is to use cloud computing. Cloud computing key benefits include scalability, instant provisioning, virtualized resources, and cost effectiveness. There are different ways to deploy cloud resources such as public, private, and hybrid cloud. Business requirements determine the best deployment model to use. In this work, we have built a simulation model based on genetic programming to find the optimal combination of private and public cloud resources to satisfy a pattern of demand over the planning period as well as the optimal guaranteed service level. Our main findings is that the optimal level of private computing capacity depends to a large extent on the shape of the demand curve, negative exponential or normally for example. Variations in demand within the same family of demand distributions have a very small effect on capacity for the same mean demand over the planning period but significant impact on capacity utilization and cost. The distinguishing feature of our model is that it can handle any theoretical or an ad hoc demand probability distribution. In addition, our computational scheme allows for any random variation in any of the parameters affecting the total cost of cloud resources consumed as long as this variation can be described by an estimated parametric or empirical probability density function. In addition, the model can be easily modified to determine the optimal total cost with respect to any parameters that can be used as decision variables. The accuracy and correctness of the model was tested against results obtained from a mathematical model based on an exponential probability distribution with almost identical results.
A Compromising Strategy Based on Constraint Relaxation for Automated Negotiating Agents

This paper presents a compromising strategy based on constraint relaxation for automated negotiating agents. Automated negotiating agents have been studied widely and are one of the key technologies for the future society where multiple heterogeneous agents are collaborately and competitively acting in order to help humans perform daily activities. For example, driver-less cars will be common in the near future. Such autonomous cars will need to cooperate and also compete with each other in traffic situations. A lot of studies including international competitions have been made on negotiating agents. A principal issue is that most of the proposed negotiating agents employ an ad-hoc conceding process, where basically they are adjusting a threshold to accept their opponents’ offers. Because merely a threshold is adjusted, it is very difficult to show how and what the agent conceded even after agreement has been reached. To address this issue, we describe an explainable concession process we propose using a constraint relaxation process. In the process, an agent changes its belief that it should not believe a certain constraint so that it can accept its opponent’s offer. We also describe three types of compromising strategies we propose. Experimental results demonstrate that these strategies are efficient.
Learning Word Representations with Deep Neural Networks for Turkish

We test different word embedding methods in Turkish. The goal is to represent related words in a high dimensional space such that their positions reflect this relationship. We compare word2vec, fastText, and ELMo on three Turkish corpora of different sizes. Word2vec works at the word level, fastText works at the character level; ELMo, unlike the other two, is context dependent. Our experiments show that fastText is better on name and verb inflection, and word2vec is better on semantic/syntactic analogy tasks. Bag-of-words model is better than most trained word embedding models on classification.
An Ensemble Learning Imbalanced Data Classification Method Based on Sample Combination Optimization

Abstract Imbalanced data classification is one of the hot topics in data mining and machine learning in recent years. In practice, Imbalanced data classification is very common, such as cancer detection, spam discrimination, credit card fraud detection, etc. Because of the large difference in the number of categories and Imbalanced distribution, traditional classification algorithms have poor classification effect on minority classes, and correct identification of minority classes often brings greater value. Therefore, how to effectively identify minority classes in Imbalanced data is of great importance. Practical significance. Aiming at the problems that the Bagging-based Imbalanced data classification method cannot guarantee the validity and existence of classification boundaries by adding redundant noise information and sampling, an ensemble learning GABagging method based on sample combination optimization is proposed. Firstly, the sample combination optimization algorithm uses genetic algorithm to select a subset from most classes and construct a new data set with a few classes. Subsequently, several sample combinatorial optimization algorithms are used to train and integrate several classifiers. The experimental results show that GABagging can improve the correct recognition ability of minority classes on 19 Imbalanced datasets compared with other similar methods such as TPR and AUC, without excessive loss of recognition ability of majority classes. It is proved that GABagging can compensate for the shortcomings of related Bagging-based methods such as easy loss, increasing samples and not guaranteeing the validity and existence of classification boundaries after sampling.
HWE

Text classification is one of the most important tasks in natural language processing and information retrieval due to the increasing availability of documents in digital form and the ensuing need to access them in flexible ways. By assigning documents to labeled classes, text classification can reduce the search space and expedite the process of retrieving relevant documents. In this paper, we propose a novel text representation method, Hybrid Word Embeddings (HWE), which combines semantic information obtained fromWord- Net and contextual information extracted from text documents to provide concise and accurate representations of text documents. The proposed HWE method can improve the efficiency of deriving word semantics from text by taking advantage of the semantic relationships extracted from WordNet with less training corpus. Experimental study on classification of documents shows that the proposed HWE outperforms existing methods, including Doc2Vec and Word2Vec, in terms of classification accuracy, recall, precision, etc.
Multi-attention Item Recommendation Model Based on Social Relations

Incorporating social relations in recommendation provides a promising way to alleviate problems of sparsity and cold start in collaborative filtering methods. However, most existing methods do not yet take into account social relations in a relative complete way. Besides the differences between preferences of friends, connection strength and expertise differences of users on a given item also have impacts on the spread of preference between friends. In this paper, we propose a social-aware recommendation model named Multi-Attention Item Recommendation model based on Social relations (MAIRS) which allows us to select more informative friends from the perspectives of their preferences, connection strengths, and expertise on items by their own respective attention models. And then, the three attention models are fused together by utilizing an aggregation function. We compare our method with state-of-the-art models on three real-world datasets: Delicious, Ciao and Epinions. The experimental results show that our method consistently outperforms state-of-the-art models in terms of several ranking metrics.
An inability to reproduce

Big data and modern analytics offer enormous possibilities for research, provided scientists can produce consistent results.
Dependency parsing of Polish

Abstract The predicate-argument structure transparently encoded in dependency-based syntactic representations supports machine translation, question answering, information extraction, etc. The quality of dependency parsing is therefore a crucial issue in natural language processing. In the current paper we discuss the fundamental ideas of the dependency theory and provide an overview of selected dependency-based resources for Polish. Furthermore, we present some state-of-the-art dependency parsing systems whose models can be estimated on correctly annotated data. In the experimental part, we provide an in-depth evaluation of these systems on Polish data. Our results show that graph-based parsers, even those without any neural component, are better suited for Polish than transition-based parsing systems.