Reviewerly/exHarmony huggingface dataset:
A modal extension of Jaśkowski’s discussive logic $\textbf{D}_\textbf{2}$

Abstract In Jaśkowski’s model of discussion, discussive connectives represent certain interactions that can hold between debaters. However, it is not possible within the model for participants to use explicit modal operators. In the paper we present a modal extension of the discussive logic $\textbf{D}_{\textbf{2}}$ that formally corresponds to an extended version of Jaśkowski’s model of discussion that permits such a use. This logic is denoted by $\textbf{m}\textbf{D}_{\textbf{2}}$. We present philosophical motivations for the formulation of this logic. We also give syntactic characterizations of the logic and propose a comparison with certain other modal systems. In particular, we prove that $\textbf{m}\textbf{D}_{\textbf{2}}$ is neither normal nor regular. On the basis of the axiomatization of $\textbf{D}_{\textbf{2}}$, we give an axiomatization of $\textbf{m}\textbf{D}_{\textbf{2}}$. We also give another axiomatization which is not based on the axiomatization of $\textbf{D}_{\textbf{2}}$. Furthermore, we give a natural Kripke-style semantics for $\textbf{m}\textbf{D}_{\textbf{2}}$ and prove the respective adequacy theorems.
Medical (CT) image generation with style

We propose the use of a conditional generative adversarial network (cGAN) to generate anatomically accurate full-sized CT images. Our approach is motivated by the recently discovered concept of style transfer and proposes to mix style and content of two separate CT images for generating a new image. We argue that by using these losses in a style transfer based architecture along with a cGAN, we can increase the size of clinically accurate, annotated datasets by multiple folds. Our framework can generate full-sized images with novel anatomy at spatial high resolution for all organs and only requires limited annotated input data of a few patients. The expanded datasets our framework generates can then be utilized within the many deep learning architectures designed for various processing tasks in medical imaging.
Fuzzy 2D Linear Discriminant Analysis Based on Sub-image and Random Sampling for Face Recognition

Face recognition, as a research hot topic, still faces many challenges. This paper proposes a new face recognition method by fusing the advantages of fuzzy set theory, sub-image method and random sampling technique. In this method, we partition an original image into some sub-images to improve the robustness to different facial variations, and extract local features from each sub-image by using fuzzy 2D-Linear Discriminant analyzis (LDA) which makes use of the class information hidden in neighbor samples. In order to increase the diversity of component classifiers and retain as much as the structural information of the row vectors, we further randomly sample row vectors from each sub-image before performing fuzzy 2D-LDA. Experimental results on Yale A, ORL, AR and Extended Yale B face databases show its superiority to other related state-of-the-art methods on the different variations such as illumination, occlusion and facial expression. Furthermore, we analyze the diversity of our proposed method by virtue of Kappa diversity-error analyzis and frequency histogram and results show that the proposed method can construct more diverse component classifiers than other methods.
Using Ontology Engineering to Design an Artificial Intelligence Course

The paper describes an ontological approach to the development of an adaptive artificial intelligence educational course. It discusses structural, methodological, and content modifications, and presents visual models of the course.
Residual displacement and residual strain measurement for steel frame under seismic load by integrated dot centroid tracking method

The applicability of the image-based method for residual displacement and strain measurement in a structure which is damaged by seismic load was experimentally investigated. A specimen of steel frame was constructed and was subjected to seismic excitation in one direction. Residual displacement and strain field in a beam-to-column connection which suffered from seismic load was measured by Integrated Dot Centroid Tracking (IDCT) method which is one of the image-based methods. A statistical processing result showed the measurement was achieved with a smaller resolution than residual displacement under a minimum seismic load case. The residual displacement due to local tensile and shear force explained with the deformed triangular mesh which made by the centroids of dots. As a comparison with other measurement methods, an elongation between two centroids of dots on beam and column was compared using a displacement transducer installed inside the beam. The measurement result by the displacement transducer validated compared with a calculated displacement waveform using acceleration. As the number of measurements is increased, both measurement results showed a tendency to increase. Differences of residual displacement by two types measuring method was within 15–20%. The series of experiment in this study revealed the applicability of measurement for real structure using IDCT method.
Dataset Generation Using a Simulated World

In this paper we focus on a missing part of solving the detection of human workers around the load and the truck of a crane. Current solution attempts use Convolutional Neural Networks to detect these workers in images. Especially the detection around the load can not be solved with the use of public datasets as the viewing angles differ to much. We therefore propose and evaluate an approach which uses a simulation engine to create automatically labeled images to train a network for this use case.
Can Categories and Attributes Be Learned in a Multi-Task Way?

Intuitively, we can think of object recognition and attribute prediction as correlated tasks. However, they appeared to conflict in a simple two-branch multi-task framework (a category branch and an attribute branch) with a shared backbone part (convolutional layers and pooling layers). The performance dropped along with the iterative training steps. This result might have been caused by the noncoherent feature distribution between the object recognition features and the attribute prediction features. Recognition features are discriminative for different categories and are not sensitive to intracategory variations, while attribute prediction features are discriminative for different attributes, although these attributes can exist in objects from the same category. Thus, a conflict occurs when we force the network to learn the two kinds of distinct features simultaneously. To address this problem, we propose the category and attribute prediction network (CAP-net), in which a category-constrained attribute prediction structure is introduced to transfer the object recognition knowledge and avoid the conflict between two features. The CAP-net parameters can be learned easily with a regularization method. Extensive experimental results show that the CAP-net outperforms the state-of-the-art methods on object recognition and attribute prediction tasks.
Software for Automation of User Requirements Prioritization

This paper describes the system for automating the user requirements prioritization URPCalc. A key feature is the presentation of user requirements as an estimation of priorities based on key characteristics of users and changes in these ratings over time. This approach allows us to estimate the change in the relevance of user requirements and forecast it for the near future. The proposed approach focuses on ensuring quality and reducing labor costs for a product manager by developing software for analyzing and prioritizing user requirements. The article presents and discusses the algorithms and architecture of the URPCalc system.
The Container Deployment Strategy Based on Stable Matching

The number of containers is usually far greater than the number of virtual machines in data center. In order to avoid the energy waste caused by the frequent migration of container after initial deployment, an efficient container initial deployment strategy is particularly important. Therefore, this paper studies a many-to-one stable matching algorithm and proposes a container placement strategy MLSM. We start at an initial hosting solution for containers to reduce their migration times by using a stable matching algorithm. This algorithm uses the similarity methods as the matching preference strategy for containers and virtual machines. The virtual machine preference list is sorted by the resource utilization rate. The simulation results show that the algorithm can reduce the energy consumption by an average of 12.8% compared with using FirstFit algorithm.
Web Navigation and Usability Analysis of Educational Websites in Pakistan

Website plays a vital role for any university. It is like the digital footprint of your physical existence because before visiting university, stakeholders usually visit their corresponding websites. The purpose of this research is to apply the trunk test to check the website navigability on the recognized private educational institutes of Pakistan to evaluate that how the websites are designed to be comfortable, navigable and usable for the user. More than hundred private sector educational websites of Pakistan are tested and evaluated using trunk test approach and the suggestion are being made to improve the navigability and usability of Pakistan's' educational websites.
A Comparison of Features for Replay Attack Detection

Abstract Speaker verification (ASV) systems are still vulnerable to different kinds of spoofing attacks, especially replay attack due to high-quality playback devices. Many countermeasures have been developed recently. Most of the efforts focus on the search for more salient features and many new features have been proposed. Five kinds of features, namely Mel-frequency cepstral coefficients (MFCCs), linear frequency cepstral coefficients (LFCCs), inverted Mel-frequency cepstral coefficients (IMFCCs), constant Q cepstral coefficients (CQCCs) and bottleneck features were compared on the public ASVspoof 2017 and BTAS 2016 datasets in this paper. Our experimental results show that MFCCs and bottleneck features yield comparable results. Both of them significantly outperform others (including the recently proposed CQCCs). However, the number of filters and cepstral bins are essential to the success of MFCCs.
Cognitive Load Drivers in Large Scale Software Development

Software engineers handle a lot of information in their daily work. We explore how software engineers interact with information management systems/tools, and to what extent these systems expose users to increased cognitive load. We reviewed the literature of cognitive aspects, relevant for software engineering, and performed an exploratory case study on how software engineers perceive information systems. Data was collected through five semistructured interviews. We present empirical evidence of the presence of cognitive load drivers, as a consequence of tool use in large scale software engineering.
Mixed Reality Lab at Mississippi State University Libraries

Virtual and Augmented Realities are advancing technologies that are becoming more popular in gaming and programming communities. However, virtual reality has been in development for decades. As libraries are often at the forefront of offering new and advancing technologies to their communities, virtual and augmented reality programs have become new additions to library services. While gaming is the prominent use of these technologies, virtual and augmented reality affords many educational opportunities. Mississippi State University Libraries have recently added a virtual/augmented reality lab as part of its library program.
Exploring the home environment: fusing rubbish and design to encourage participant agency and self-reflection

This paper presents an in-home design study investigating the resource conservation strategies of 40 frugal households located across urban and regional Australia. The purpose of this paper is to reflect on the method of design to explore the home environment, discussing how this approach may benefit researchers looking to investigate the home. We provide a reflexive account of the design process undertaken in this study, and present a discussion into the benefits of using this approach. The contribution of this paper reflects on two main aspects: 1) Designing for the culture of the home; and 2) Using design to encourage participant agency and self-reflection.
A novel camera path planning algorithm for real-time video stabilization

In video stabilization, a steady camera path plan is as important as accurate camera motion prediction. While several camera path planning algorithms have been studied, most algorithms are used for post-processing video stabilization. Since all of the frames are accessible in post-processing video stabilization, a camera path planning method can generate a good camera path, considering the camera movements at all of the frames. Meanwhile, the number of accessible frames in real-time video stabilization is limited. Thus, smooth camera path planning is a challenging issue in real-time video stabilization. For example, a camera path planner does not know in advance the locations of sudden camera motions, so it is not easy to compensate a sudden camera movement. Therefore, this paper proposes a novel camera path planning algorithm for real-time video stabilization. A camera path planning method should fully utilize the given image margin to provide steady camera paths. In contrast, it should retain the certain amount of an unused image margin for use in frames with dynamic or sudden camera movements. To resolve this problem, the proposed algorithm uses two terms related to the steady camera path and the amount of image margin, and it cross-optimizes the two terms to provide a new camera path on-the-fly. Experimental results show that the proposed algorithm provides excellent performance in real-time video stabilization while requiring negligible computation.
Multi-layer Convolutional Neural Network Model Based on Prior Knowledge of Knowledge Graph for Text Classification

Deep neural networks can obtain effective hierarchical representations, which make them perform well in image recognition and speech recognition. However, the disadvantages of deep learning relying on large-scale annotation data also limit its development. To this end, a multi-layer convolutional neural network based on knowledge graph prior knowledge is proposed in this paper, which acquires text features from finer granularity, and using the existing prior knowledge which increases granularity information to enhance the semantic information of the text, and to reduce the dependence of the model on large-scale samples. At the same time, it can reduce the over-fitting problem of the model, and improve classification accuracy. This model performs well in several large data sets. By introducing prior knowledge to TextCNN, a classical deep neural model, it has also verified the validity of using prior knowledge through experiments.
Estimation of situation awareness score and performance using eye and head gaze for human-robot collaboration

Human attention processes play a major role in the optimization of human-robot collaboration (HRC) [Huang et al. 2015]. We describe a novel methodology to measure and predict situation awareness from eye and head gaze features in real-time. The awareness about scene objects of interest was described by 3D gaze analysis using data from eye tracking glasses and a precise optical tracking system. A probabilistic framework of uncertainty considers coping with measurement errors in eye and position estimation. Comprehensive experiments on HRC were conducted with typical tasks including handover in a lab based prototypical manufacturing environment. The gaze features highly correlate with scores of standardized questionnaires of situation awareness (SART [Taylor 1990], SAGAT [Endsley 2000]) and predict performance in the HRC task. This will open new opportunities for human factors based optimization in HRC applications.
Partially APN Boolean functions and classes of functions that are not APN infinitely often

In this paper we define a notion of partial APNness and find various characterizations and constructions of classes of functions satisfying this condition. We connect this notion to the known conjecture that APN functions modified at a point cannot remain APN. In the second part of the paper, we find conditions for some transformations not to be partially APN, and in the process, we find classes of functions that are never APN for infinitely many extensions of the prime field $\mathbb {F}_{2}$, extending some earlier results of Leander and Rodier.
From positive PDL to its non-classical extensions

Abstract We provide a complete binary implicational axiomatization of the positive fragment of propositional dynamic logic (PDL). The intended application of this result are completeness proofs for non-classical extensions of positive PDL. Two examples are discussed in this article, namely, a paraconsistent extension with modal De Morgan negation and a substructural extension with the residuated operators of the non-associative Lambek calculus. Informal interpretations of these two extensions are outlined.
Context-aware factorization machine for recommendation in Massive Open Online Courses(MOOCs)

Personalization in the field of e-learning is a topic that receives a lot of interest from researchers. In the same time, the Massive Open Online Course(MOOCs) have witnessed fast development in recent years due to their high flexibility. In this paper, we aim to create a contextual modelling recommender system in a MOOC platform that relies on analysing cognitive acquisition of learners across the learning period. Using context-aware factorization machine algorithm, our approach is designed to be sensitive to the characteristics of each individual learner since this last have different intellectual capabilities, skills, modes of learning, preferences, and needs; in order to make the suitable decision corresponding to each learner's profile.
FPGA Implementation of Crossover Module of Genetic Algorithm

This paper proposes a hardware realization of the crossover module in the genetic algorithm for the travelling salesman problem (TSP). In order to enhance performance, we employ a combination of pipelining and parallelization with a genetic algorithm (GA) processor to improve processing speed, as compared to software implementation. Simulation results showed that the proposed architecture is six times faster than the similar existing architecture. The presented field-programmable gate array (FPGA) implementation of PMX crossover operator is more than 400 times faster than in software.
Integrated network and hosts energy management for cloud data centers

Abstract Cloud computing provides online services to users from anywhere in a pay‐as‐you‐go basis. This leads to the construction of different forms of data centers around the world, which usually consumes huge amount of energy and produce pollution (heat and carbon dioxide footprint). Many efforts have focused on managing cloud resources to decrease their energy consumption. Since servers are the highest energy consumers in data centers, most researchers have focused on optimizing their energy consumption while other resources such as network devices and cooling systems have received less attention. In this paper, we propose a network‐aware virtual machine (VM) relocation algorithm, which optimizes the distribution of the VMs running on the data center in order to conserve energy in both servers and network devices (switches). The proposed technique targets the communicated VMs and places them closer to each other. This would reduce the traffic overhead between any communicating VMs so that fewer network devices are involved in the communication process. This also increases the VMs consolidation to some servers while leaving other servers idle. Switching off the idle servers and network devices would significantly reduce the total energy consumed by the data center. The proposed algorithm is evaluated using simulation and the performance of the proposed algorithm is compared against different previously proposed algorithms in the literature. Performance metrics used are the energy consumed by the host and by the network switches, the service level agreement violations, and the number of VM migrations. The impact of different packet sizes, the efficiency of switching the network elements to sleep mode, and the impact of resource homogeneity and heterogeneity are also studied and discussed. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of all evaluation metrics.
Spatial-aware correlation filters with adaptive weight maps for visual tracking

Recently discriminative correlation filter (DCF) methods demonstrate excellent performance for visual tracking. These methods, however, often suffer from boundary effects, especially when the target search area is small. To mitigate boundary effects, a larger target search area is chosen, which, however, introduces many background pixels so that the tracking filter is contaminated by the introduced background pixels and the tracking performance is degraded. In order to alleviate the effects of the background pixels, this paper proposes a spatial-aware adaptive weight map, which wisely assigns large weights to pixels of the target and small weights to pixels in the background. Such desired weight map is adaptively generated by combining the target likelihood, which quantitatively measures whether a pixel belongs to the target or the background, and prior spatial weights of pixels. By integrating the spatial-aware adaptive weight map into the DCF framework, the obtained DCF tracking method can achieve more precise target location and suffer from less tracking drift than those conventional tracking methods. Moreover, we compute a confidence score from the response map and train the tracking filter only in the high-confidence situation. This high-confidence updating strategy can effectively avoid the tracking filter corruption problem when the target is severely occluded and the target search area is mainly filled with pixels of the background. The proposed tracking method is compared with some state-of-the-art methods through extensive experiments on four benchmark datasets, which confirm the performance superiority of the proposed one.
Study of E-Learning System Based on Cloud Computing: A Survey

It is worth to mention that nowadays we observe an evolving interest for the acquisition and the exploitation of new technologies in the context of learning specifically the Internet. They are based on the use of approaches with diverse functionalities (e-mail, Web pages, forums, LMS, and so on) as a support of the process of teaching-learning. E-learning systems usually require many hardware and software resources. There are many education institutions that cannot afford such investments, and cloud computing is the best solution. Cloud computing is the basic environment and platform of the future e-learning. This paper mainly focuses on the application of cloud computing in e-learning environment.