Reviewerly/exHarmony huggingface dataset:
A Filter Based Feature Selection for Imbalanced Text Classification

In this work, a text classification method through a filter type feature selection for imbalanced data is addressed. The model initially clusters the documents associated with a class through a hierarchical clustering there by accomplishing a balanced or near balanced class. Later, a filter type feature selection is recommended to choose the most discriminative features for text classification. Subsequently, the documents are stored in the form of interval valued data. For classification purpose, a suitable symbolic classifier is recommended. The experimentation is done with two standard benchmarking datasets viz., Reuters 21578 and TDT2. The experimental results obtained from the proposed model are better in terms of f-measure when compared to the available models.
Cybersickness and Postural Sway Using HMD Orientation

Virtual reality (VR) systems have become widespread in recent years. However, feelings of motion sickness-like symptoms, or cybersickness, is a significant concern. Currently, there is no reliable, objective method to dynamically monitor cybersickness in consumer systems. This longitudinal study expands on prior postural stability research to permit fewer disruptions. The motivation is to find objective measures for monitoring that is applicable in a wide variety of consumer head mounted displays (HMD). This study’s results suggest that postural sway is correlated with cybersickness even without the restriction of the Tandem Romberg stance using HMD location data. To accomplish this, the correlation of cybersickness to the movement of the HMD without suspending VR use was examined. As in past studies the HMD horizontal variance correlates with simulator sickness questionnaire (SSQ). Other significant correlations were found in the HMD fore-aft translation, HMD pitch, and HMD roll. These results reveal that measuring postural sway could lead to warning VR users if they are at risk for becoming ill.
Segmentation and Recovery of Pathological MR Brain Images Using Transformed Low-Rank and Structured Sparse Decomposition

We present a common framework for the simultaneous segmentation and recovery of pathological magnetic resonance (MR) brain images, where low-rank and sparse decomposition (LSD) schemes have been widely used. Conventional LSD methods often produce recovered images with distorted pathological regions, due to the lack of constraint between low-rank and sparse components. To address this issue, we propose a transformed low-rank and structured sparse decomposition (TLS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> D) method, which is robust for extracting pathological regions. Moreover, the well recovered images can be obtained using both structured sparse and computed image saliency as the adaptive sparsity constraint. Experimental results on MR brain tumor images demonstrate that our TLS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> D can effectively provide satisfactory performance on both image recovery and tumor segmentation.
A Structured Argumentation Framework for Modeling Debates in the Formal Sciences

Scientific research in the formal sciences comes in multiple degrees of formality: fully formal work; rigorous proofs that practitioners know to be formalizable in principle; and informal work like rough proof sketches and considerations about the advantages and disadvantages of various formal systems. This informal work includes informal and semi-formal debates between formal scientists, e.g. about the acceptability of foundational principles and proposed axiomatizations. In this paper, we propose to use the methodology of structured argumentation theory to produce a formal model of such informal and semi-formal debates in the formal sciences. For this purpose, we propose ASPIC-END, an adaptation of the structured argumentation framework ASPIC+ which can incorporate natural deduction style arguments and explanations. We illustrate the applicability of the framework to debates in the formal sciences by presenting a simple model of some arguments about proposed solutions to the Liar paradox, and by discussing a more extensive—but still preliminary—model of parts of the debate that mathematicians had about the Axiom of Choice in the early twentieth century.
Three Dimensional Posed Face Recognition with an Improved Iterative Closest Point Method

Dealing with different head poses is one of the most challenging issues in the area of face recognition. Recently, 3D images have been used for this purpose as they can gather more information from the head area. Kinect was used for capturing 3D images in our research. Iterative Closest Point (ICP) algorithm has been used in many researches to align a rotated pointcloud with its corresponding reference. However it has many variables that can improve its performance. So an improved version of ICP has been introduced in our research and its performance in terms of accuracy and speed has been evaluated. While it can have many applications, we have used it for increasing the performance of posed face recognition. We applied our proposed algorithm on a local database and concluded that it can significantly improve the recognition rate of 3D posed face recognition compared with using original raw posed image. Results of executing the proposed algorithm on a public database also indicate an improvement with respect to other recently proposed methods.
Uniqueness of Minimax Strategy in View of Minimum Error Discrimination of Two Quantum States

This study considers the minimum error discrimination of two quantum states in terms of a two-party zero-sum game, whose optimal strategy is a minimax strategy. A minimax strategy is one in which a sender chooses a strategy for a receiver so that the receiver may obtain the minimum information about quantum states, but the receiver performs an optimal measurement to obtain guessing probability for the quantum ensemble prepared by the sender. Therefore, knowing whether the optimal strategy of the game is unique is essential. This is because there is no alternative if the optimal strategy is unique. This paper proposes the necessary and sufficient condition for an optimal strategy of the sender to be unique. Also, we investigate the quantum states that exhibit the minimum guessing probability when a sender's minimax strategy is unique. Furthermore, we show that a sender's minimax strategy and a receiver's minimum error strategy cannot be unique if one can simultaneously diagonalize two quantum states, with the optimal measurement of the minimax strategy. This implies that a sender can confirm that the optimal strategy of only a single side (a sender or a receiver but not both of them) is unique by preparing specific quantum states.
Dynamic Non-Classicality

I show that standard dynamic approaches to the semantics of epistemic modals invalidate the classical laws of excluded middle and non-contradiction, as well as the law of ‘epistemic non-contradiction’. I argue that these heretofore unnoticed facts pose a serious challenge for these approaches.
Segmenting New Image Acquisitions Without Labels

We are interested in solving the problem of segmentation when no gold-standard labels are available for new image acquisition protocols. We developed a dual generative adversarial network (GAN), called Synth-GAN, which incorporates a differential operator loss (to favor retaining edges), as well as cyclic loss (to guarantee reconstruction of inputs). We show how the developed approach facilitates the application of an automated deep learning approach trained on one type of image (T2-weighted fat-sat MR) to be successfully applied to images well outside the trained distribution (Tl-weighted MR). A total of 100 images of each sequence from different patients were used (80% for training), and performance of the method was assessed by comparing how the previously developed automated segmentation approach performed prior to and post application of Synth-GAN. The developed approach improved the DICE coefficient from 0.39 (applying the automated segmentation method to the original Tl images) to 0.74 (applying the segmentation method to the synthesized T2 images). This approach will be useful for generalizing automated approaches across modalities, and institutions, when differences in hardware and software significantly alter image representations.
How to Compare and Exchange Facial Expression Perceptions Between Different Individuals with Riemann Geometry

Facial expression recognition has been a major theme in researches of human-centric science and technology. An open question remained is that it is difficult to find an objective representation for facial expressions so that one can compare perceptions between different individuals. It is partially due to that psychological spaces of facial expressions until now were built from subjective evaluations such as SD score or Affective grid, in which it is difficult to find correspondence between physical stimuli and psychological responses. Recently, Sumiya et al. built a psychophysical facial expression space by measuring JND thresholds in the facial image space, which define the space as a Riemann manifold [7]. In this paper, we present algorithms to compare and exchange facial expression perceptions between individuals using isometry or a distance-preserving map between the Riemann spaces.
Using Client-Based Class Cohesion Metrics to Predict Class Maintainability

It has been established that there is a strong positive correlation between class cohesion (what we call internal connection-based class cohesion) and class maintainability. Unfortunately, the potential for using internal connection-based class cohesion to predict class maintainability is limited because we cannot measure this class cohesion until the class has been designed and coded. In this paper, we define client-based class cohesion which can be measured in the design phase. Thus, we are able in the design phase to use class cohesion to predict class maintainability. Further, the potential exists that measurements of this client-based class cohesion may in some ways be more useful than measurements of internal connection-based cohesion. This may be true because in addition to involving factors internal to the class in question, external factors, i.e., client classes, are also involved. We conduct an empirical study to investigate the extent to which a client-based class cohesion metric can be used individually and in combination with other internal connection-based class cohesion metrics to predict class maintainability. The results of the study indicate that the client-based class cohesion metric is very promising and may lead to improved results.
Achieving Efficient and Privacy-Preserving Top-k Query Over Vertically Distributed Data Sources

Data collected from various data sources are destined to be logically interrelated but geographically distributed. Top-k query is an efficient way to find the most important objects from high volumes of data. A common way to process the top-k query over distributed data is to bring them to a centralized entity (e.g. cloud). However, there are privacy considerations during the top-k query when dealing with sensitive data (e.g. eHealthcare data) in such method. Apart from data privacy, efficiency also needs to be taken into consideration. Existing focuses on top-k query do not (fully) consider the data privacy or efficiency. In order to deal with the mentioned disadvantages, in this paper, we propose an efficient and privacy-preserving top-k query scheme over vertically distributed data. Specifically, we first design a data filtering technique to reduce the number of transmitted data from each data source to the centralized entity, which can greatly reduce the communication overhead and computational cost. Then, we propose a privacy-preserving top-k query scheme over encrypted data by deploying the homomorphic encryption technique, which can well preserve the private information and achieve the functionality at the same time. Besides, security analysis shows that the proposed scheme is privacy-preserving and performance evaluation validates the efficiency of the proposed scheme.
Neural Detection of Foreign Objects for Transmission Lines in Power Systems

Ensuring the normal operation of the transmission lines, which provides a path for directing the transmission of energy from one place to another, is a prerequisite for delivering power to cities and enterprises. A major threat comes from foreign objects, which may cause interruption of power transmission. Compared with traditional manual method, which not only consumes a lot of manpower, but more importantly, affects the safety and efficiency of power network, in this paper, we apply a neural detection of foreign objects for transmission lines. Transfer learning and data augmentation are used to mitigate data shortages. Experimental results show that even with small training data, the neural detection with transfer learning and data augmentation is an effective method for this task without loss of real-time property.
Adaptive Instructional Systems: The Evolution of Hybrid Cognitive Tools and Tutoring Systems

This paper begins with a discussion on framing technology as tools that facilitate a distributed cognition for problem solving and supporting discriminating minds. Specifically, Dewey’s advocacy of using technology as a learning tool to support the development of discriminating minds will serve as a framework for understanding the purpose and aim of this next generation of Adaptive Instructional Systems (AISs). This paper reviews the evolution of computer assisted instruction, distinguishing between the iterations of early computer cognitive tools to more effective intelligent tutoring systems, followed by next generation hybrid systems, or AISs, that are a unique blend of cognitive tools and intelligent tutoring. These AISs are the result of improved technological affordances combined with research in education to achieve meaningful learning and discriminate intelligence, an objective aligned with Dewey’s framework for using technology as a learning tool.
Source Coding at the Edge: User Preference Oriented Lossless Data Compression

Source coding is an efficient technique to save data storage and transmission costs. Traditional source coding methods are usually designed based on the statistical distribution of symbols generated by the information source. Notice that, in content-centric networks, the user preference has a significant impact on the statistical distribution of symbols transmitted in the links. This paper presents an edge source coding method to compress data in the network edge. An optimization problem is formulated to obtain the optimal codebook design. Based on the solution of the single codebook case, an optimality condition for the optimization problem is presented. For discrete user preferences, a variant of the k-means++ algorithm is used to give a suboptimal solution. For continuous user preferences, two algorithms are proposed to give codebook designs. Both theoretical analysis and simulations demonstrate the optimal codebook design should take into account the user preferences.
Ismael: Using Machine Learning to Predict Acceptance of Virtual Clusters in Data Centers

Existing virtual network admission control algorithms targeting high utilization of data center infrastructure are computationally expensive or provide poor performance. In particular, existing algorithms have in common that they are oblivious to the past, i.e., requests are handled in a fire-and-forget manner, not taking into account information from previously solved instances. This can be inefficient and misses out on a basic optimization opportunity: as for any network optimization algorithm that faces repeating problem instances, it may be beneficial to learn from network states and the outcome of acceptance decisions of the past. In this paper, we propose Ismael, a machine learning framework for predicting the acceptance of virtual clusters, one of the most common virtual network abstractions in data centers. Ismael can be configured with, and learn from, different existing algorithms by combining fixed-size feature representations for graphs with a convolutional neural network or a fully connected deep neural network. We report on extensive simulations, which demonstrate that it is possible to mimic existing, computationally intensive admission control algorithms with an accuracy of up to 94 %, while significantly reducing runtime.
Measuring the effects of emojis on Turkish context in sentiment analysis

Automatic detection of sentiments is considered among complex problems in social applications. In information security, emojis are used in several interfaces for user authentication, antropomorphic secure access and remote communication. The use of emojis in multimodal information triggers new challenges in complex networks and mobile security applications. The fast growth of social media, microblogs, floods expands the definition of sentimental analysis where the extraction of emotions from user posts becomes a cutting edge. Therefore, the opinion mining becomes a crucial step for the analysis of social behaviour in individuals or groups for the detection of trends. In current applications, the language of emojis is considered as a common way or an interlingua to express the ideas or intensify feelings. However, there are few studies to reveal its effects on Turkish context for overlapped and separate senses. In this study, emojis have been classified as a parameter of textual descriptions for the emotions in Turkish language. The emotion analysis has been performed by Support Vector Machines (SVM) and multinomial Naive Bayes (NB) using test and train sets derived from Twitter corpus. The preparation and preprocessing of the corpus have been accomplished by generating the classifiers; groups and emotions. The neutral emotion state has been also added to compare the accuracy levels in classification. The use of corpus in a generic domain present a promising field where different emotion states have been measured. The evaluation scores indicate that SVM would perform better and neutral emotional emojis might decrease total accuracy in Turkish language.
Mining class association rules on imbalanced class datasets

The task of discovering sets of good rules from imbalanced class datasets may not come easy for existing class association rule mining algorithms. The reason is that they often generate rules belonging to the dominant classes. For example, in medical applications, some symptoms of illness are not p opular, and the doctors are very interested in the rules associated with these symptoms. This paper proposes a novel approach for mining class association rules (CARs) in imbalanced class datasets. Firstly, assuming there are n given classes, the training dataset is split into n corresponding groups. For each group, the data is clustered by the k-means algorithm into k groups where the value of k is equal to the number of records of the smallest group. Secondly, we combine all records from the groups after clustering and use the CAR-Miner-Diff algorithm to mine all CARs. We also propose an iterative method to get a highly accurate classifier. From experiments, we show that the proposed approach outperforms existing algorithms while maintaining a large number of useful rules in the classifier.
Unity based Urban Environment Simulation for Autonomous Vehicle Stereo Vision Evaluation

Testing and evaluation of sensor processing algorithms for autonomous vehicles is challenging due to the problem of collecting reference data. To ensure safety and robustness, many man-hours need to be spent in collecting and preparing such data. One solution to alleviate this problem is to use computer simulations. Computer simulations can model a real system with all its static and dynamic characteristics. This approach provides efficiency and precision when collecting data and reduces testing time. The aim of this paper is development of a simulation environment based on Unity where it would be possible to test sensors and algorithms for autonomous vehicles and show deviations from reference data. The proposed simulation model contains typical city objects and participants: roads, sidewalks, buildings, pedestrians, traffic signs and vehicles. In this paper we simulate motion and sensors from a single vehicle equipped with a stereo camera setup. The program environment Unity is used for designing the simulation, and behavioral scripts are executed with C# programming language. To showcase the testing of applicable algorithms, OpenCV class for computing stereo correspondence, using the semi-global block matching algorithm, is used on simulated stereo images and we discuss future development of the simulation.
Optimising trotter-suzuki decompositions for quantum simulation using evolutionary strategies

One of the most promising applications of near-term quantum computing is the simulation of quantum systems, a classically intractable task. Quantum simulation requires computationally expensive matrix exponentiation; Trotter-Suzuki decomposition of this exponentiation enables efficient simulation to a desired accuracy on a quantum computer. We apply the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) algorithm to optimise the Trotter-Suzuki decompositions of a canonical quantum system, the Heisenberg Chain; we reduce simulation error by around 60%. We introduce this problem to the computational search community, show that an evolutionary optimisation approach is robust across runs and problem instances, and find that optimisation results generalise to the simulation of larger systems.
Framework-Oriented Approach to Ease the Development of Ambient Assisted-Living Systems

Ambient assisted-living (AAL) systems, including wearables and home automation, could improve the quality of life of fragile people. As analyzed in this paper, the typical architecture of AAL systems is based on wireless sensors networks and proprietary gateways and it can be complex, expensive, and hardly includes interoperable multivendor devices. Emerging Android-based gateways can help in reducing costs, but flexibility and interoperability issues will still remain. On the basis of this fact, the development of an AAL system by a framework-oriented approach is presented. Thanks to the use of the SAndroidE framework, the proposed approach leads to the reduction of the programming effort as well as improves interoperability among different vendors ensuring high customization levels. Results obtained in a typical use case indicate that the programming effort in integrating multivendor devices is reduced by a factor of ten. The presented approach paves the way to the implementation of new open multivendor AAL systems, also encouraging redundant architectures and improving robustness of these emerging solutions.
Protecting the Internet of Things with Security-by-Contract and Fog Computing

Nowadays, the Internet of Things (IoT) is a consolidated reality. Smart homes are equipped with a growing number of IoT devices that capture more and more information about human beings lives. However, manufacturers paid little or no attention to security, so that various challenges are still in place. In this paper, we propose a novel approach to secure IoT systems that combines the concept of Security-by-Contract (S×C) with the Fog computing distributed paradigm. We define the pillars of our approach, namely the notions of IoT device contract, Fog node policy and contract-policy matching, the respective life-cycles, and the resulting S×C workflow. To better understand all the concepts of the S×C framework, and highlight its practical feasibility, we use a running case study based on a context-aware system deployed in a real smart home.