Reviewerly/exHarmony huggingface dataset:
From Conceptual Mash-ups to Badass Blends: A Robust Computational Model of Conceptual Blending

Conceptual blending is a complex cognitive phenomenon whose instances range from the humdrum to the pyrotechnical. Most remarkable of all is the ease with which we humans regularly understand and produce complex blends. While this facility will doubtless elude our best efforts at computational modeling for some time to come, there are practical forms of conceptual blending that are amenable to computational exploitation right now. In this chapter we introduce the notion of a conceptual mash-up, a robust form of blending that allows a computer to creatively reuse and extend its existing commonsense knowledge of a topic. We show also how a repository of such knowledge can be harvested automatically from the web, by targeting the casual questions that we pose to ourselves and to others every day. By acquiring its world knowledge from the questions of others, a computer can eventually learn to pose introspective questions of its own, in the service of its own creative mash-ups.
Using the Mobile Studio to Facilitate Non-Traditional Approaches to Education and Outreach

STEM education at all levels from kindergarten through grad school generally requires expensive, complex equipment and thus is typically built around elaborate facilities with well-trained staff support.Mobile Studio Pedagogy (using the Mobile Studio Desktop software and the I/O board) makes it possible for instructors and students to participate in hands-on learning to any place they have a computer.Mobile Studio gives them access, at any time and any place, to a full electronics laboratory for the price of a textbook; students have a portable lab in which tinkering is again possible; requiring only a spark of interest -not a big budget.With a good start provided by interested and dedicated teachers, student accomplishments are only limited by their imagination.Since the Mobile Studio provides a portable lab facility, students can apply the tools they use in the classroom in their personal projects.The most obvious examples involve tinkering with cars (e.g., finding security system workarounds for someone building a car from an early 90's GM drivetrain) and robotics (e.g., adding a real electrical engineering component to a project for a robotics competition).Less obvious are the kinds of activities that show how exciting engineering can be provided by our students that make us so proud of them (e.g.finishing that great idea they had in their high school science fair on their own or a handicapped driver visiting science classes in their old high school).When students share these real world experiences, they are providing the best kind of motivation for future STEM professionals.When using the Mobile Studio, teachers are also free to reconfigure their courses, especially those with significant lab/experimental content, in a number new ways.In addition, many instrumentation-based course offerings can now be held in normal classrooms rather than in specially outfitted facilities.It is no longer necessary to tie students and instructors to the usual lectures, recitations and labs, especially since it is possible to give hardware-based homework.When the Mobile Studio approach is expanded to include video lectures, we are now free to organize courses in ways we would never have imagined in the past.Mobile Studio Pedagogy has been deployed and utilized in several electrical engineering, general engineering and physics courses to, for example, better enable instructors to employ the Kolb cycle of learning in their courses.Inertia from both students and faculty concerning the need for new types of learning materials can reduce the acceptance and effectiveness of new methodologies, but significant progress, demonstrated through an extensive evaluation process, has been made at several institutions.
Use of Distributed Semi-Supervised Clustering for Text Classification

Text classification is an important way to handle and organize textual data. Among existing methods of text classification, semi-supervised clustering is a main-stream technique. In the era of ‘Big data’, the current semi-supervised clustering approaches for text classification generally do not apply for excessive costs in scalability and computing performance for massive text data. Aiming at this problem, this study proposes a scalable text classification algorithm for large-scale text collections, namely D-TESC by modifying a state-of-the-art semi-supervised clustering approach for text classification in a centralized fashion (TESC). D-TESC can process the textual data in a distributed manner to meet a great scalability. The experimental results indicate that (1) the D-TESC algorithm has a comparable classification quality with TESC, and (2) outperforms TESC by average 7.2 times by using eight CPU threads in terms of scalability.
A Two-Stage Noise Level Estimation Using Automatic Feature Extraction and Mapping Model

In this letter, a two-stage noise level estimation (NLE) algorithm that jointly exploited automatic feature extraction and mapping model was proposed. In contrast to existing NLE algorithms using hand-crafted features, we first utilized convolutional neural network-based model to automatically extract the noise level-aware features (NLAFs) in form of feature vector to characterize the distortion degree of a noisy image, i.e., noise level. Then, the NLAF vector was directly mapped to its corresponding noise level via pretrained mapping model, obtaining a fast and reliable NLE algorithm. Extensive experimental results show that the proposed NLE algorithm works well for a wide range of noise levels, showing a good compromise between speed and accuracy.
Fast Adjustable Threshold for Uniform Neural Network Quantization

The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real-life applications require simplification and acceleration of quantization procedure that will maintain the accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2, and MNAS. Here we present two methods to significantly optimize the training with quantization procedure. The first one is introducing the trained scale factors for discretization thresholds that are separate for each filter. The second one is based on mutual rescaling of consequent depth-wise separable convolution and convolution layers. Using the proposed techniques, we quantize the modern mobile architectures of neural networks with the set of train data of only 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available at: https://github.com/agoncharenko1992/FAT-fast-adjustable-threshold. Take-aways Describes ways how to get an 8-bit quantized network. The main idea is that simple min/max quantization with calibration works poor because of outliers which spoils thresholds of quantization. We can adjust this thresholds by using Straight-Through Estimators. Using some tips such as Batch Normalization folding and, channel equalization (more details you can found in the paper) we can get solution as good as training with quantization from scratch but with less data and way faster.
Addressing the Under-Translation Problem from the Entropy Perspective

Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance in recent years. However, the under-translation problem still remains a big challenge. In this paper, we focus on the under-translation problem and attempt to find out what kinds of source words are more likely to be ignored. Through analysis, we observe that a source word with a large translation entropy is more inclined to be dropped. To address this problem, we propose a coarse-to-fine framework. In coarse-grained phase, we introduce a simple strategy to reduce the entropy of highentropy words through constructing the pseudo target sentences. In fine-grained phase, we propose three methods, including pre-training method, multitask method and two-pass method, to encourage the neural model to correctly translate these high-entropy words. Experimental results on various translation tasks show that our method can significantly improve the translation quality and substantially reduce the under-translation cases of high-entropy words.
A unifying computational model of decision making

Decision making has long been of interest as a descriptive phenomenon in psychology and as a generative one in artificial intelligence. Research ranges from general, descriptive models of heuristic decision making to detailed studies of decision parameters. This paper introduces a model that formalizes and integrates several descriptive models so that it can serve both as a framework for psychological models and as an algorithm for computational decision making. We set special focus on the instantiation of this model with respect to aggregating cue values by reviewing some methods from the field of computational social choice. To show its applicability in the context of artificial intelligence we present a case study of computational problem solving.
Fuzzy-NN approach with statistical features for description and classification of efficient image retrieval

Image retrieval based on content not only relies heavily upon the type of descriptors, but on the steps taken further. This has been an extensively utilized methodology for finding and fetching out images from the big database of images. Nowadays, a number of methodologies have been organized to increase the CBIR performance. This has an ability to recover pictures relying upon their graphical information. In the proposed method, Neuro-Fuzzy classifier and Deep Neural Network classifier are used to classify the pictures from a given dataset. The proposed approach obtained the highest accuracy in terms of Precision, Recall, and F-measure. To show the efficiency and effectiveness of proposed approach, statistical testing is used in terms of standard deviation, skewness, and kurtosis. The results reveal that the proposed algorithm outperforms other approaches using low computational efforts.
Optimization for Better World University Rank

World university rankings are very competitive among educational institutions since it is a tool to attract good resources such as staff and students to the institutions. Many institutions realize the importance and require to set up to a better rank. The aim of this paper was to find how individual institution’s performance can be enhanced by finding the optimal values for each of our studied indicators: faculty student’s ratio, citations per faculty, proportion of international faculty and proportion of international students, that maximizes the overall score of Quacquarelli Symonds World University Ranking and therefore will lead to a better rank. Those four indicators are commonly used in most university ranking systems and considered to be controllable. An approach of optimization using maximization of nonlinear programming problem in which the objective function was constructed from normalization and weighting was applied throughout this research. The optimal values were obtained following Prince of Songkla University context. This research has shown that the final decision for the optimal values is based on constraints setting which depends on context, ability and policy of an individual educational institution.
Balanced Decoupled Spatial Convolution for CNNs

In this paper, we are interested in designing lightweight CNNs by decoupling the convolution along the spatial and channel dimension. Most existing decoupling techniques focus on approximating the filter matrix through decomposition. In contrast, we provide a decoupled view of the standard convolution to separate the spatial information and the channel information. The resulting decoupled process is exactly equivalent to the standard convolution. Inspired from our decoupled view, we propose an effective structure, balanced decoupled spatial convolution (BDSC), to relax the sparsity of the filter in spatial aggregation by learning a spatial configuration and reduce the redundancy by reducing the number of intermediate channels. We also designed an adaptive spatial configuration, which is simply adding a nonlinear activation layer [rectified linear units (ReLU)] after the intermediate output. Our experiments verify that the adaptive spatial configuration can improve the classification performance without extra cost. In addition, our BDSC achieves comparable classification performance with the standard convolution but with a smaller model size on Canadian Institute for Advanced Research (CIFAR)-100, CIFAR-10, and ImageNet. To show the potential of further reducing the redundancy of across channel-domain convolution, we also show experiments of our models with a designed lightweight across channel-domain convolution. Finally, we show in our experiments that our models achieve superior performance than the state-of-the-art models.
Combinatorial Bayesian Optimization using Graph Representations.

This paper focuses on Bayesian Optimization - typically considered withcontinuous inputs - for discrete search input spaces, including integer,categorical or graph structured input variables. In Gaussian process-basedBayesian Optimization a problem arises, as it is not straightforward to definea proper kernel on discrete input structures, where no natural notion ofsmoothness or similarity could be provided. We propose COMBO, a method thatrepresents values of discrete variables as vertices of a graph and then use thediffusion kernel on that graph. As the graph size explodes with the number ofcategorical variables and categories, we propose the graph Cartesian product todecompose the graph into smaller sub-graphs, enabling kernel computation inlinear time with respect to the number of input variables. Moreover, in ourformulation we learn a scale parameter per subgraph. In empirical studies onfour discrete optimization problems we demonstrate that our method is on par oroutperforms the state-of-the-art in discrete Bayesian optimization.
Study of A Drone’s Payload Delivery Capabilities Utilizing Rotational Movement

This paper describes a novel way to reliably deliver packages using a drone. For years, companies such as Amazon and Google have been hard at work developing a safe and practical way of utilizing the potential of unmanned aerial vehicles to improve upon their current network of delivery services. Transportation of even the largest package has become feasible due to the advances in drone-based robotics. The biggest problem left unanswered is how to best release the package once the drone has arrived at the drop off location. The solution presented in this paper details a basket that is capable of holding onto the package until the drone is rapidly flipped upside down in an aerial maneuver (oftentimes called a roll) and the centrifugal force ejects the payload from the basket. A practical, inexpensive, and robust solution to this problem was devised, modeled, researched, and tested with a 100% success rate. This solution will provide aerial robotics professionals with an alternative to the more standard method of using additional motors to actuate a payload delivery, thus cutting down on the cost, complexity, and failure rate associated with drone delivery. This paper will also present the design and manufacturing philosophy of the drone, and video clips will demonstrate the performance of the drone.
Moving Target Tracking Using TDOA and FDOA Measurements from Two UAVs with Varying Baseline

This paper considers the problem of tracking a moving target using the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained at two unmanned aerial vehicles (UAVs) with varying baseline. Accumulation time is necessary because the emitter position cannot be estimated at each emission when there are only two UAVs as sensors. And the target position and velocity estimation accuracy often suffer from low convergence in conventional formation flight mode no matter what nonlinear filtering algorithm is used. Based on the analysis of the influence of different flight modes on the positioning performance, a moving target tracking system from two UAVs with varying baseline is proposed. The performance of unscented Kalman filter (UKF) under proposed system is analysed and compared with the Cramer-Rao lower bound (CRLB). Simulation results show that the proposed system can speed up the convergence.
Analysis and correction of distortions in a spatial heterodyne spectrometer system

In this paper a method for correcting the radial distortion of interferograms generated by a spatial heterodyne spectrometer system is presented. Instead of utilizing calibration patterns, the distortion model parameters are estimated based on the distorted fringe features generated by projecting the straight interference stripes onto the detector. Comparisons between polynomial models and division models indicate that division models can deliver competitive performance on the reconstructed image with fewer parameters. Simulated interferograms based on ray-tracing are used to demonstrate the correction of errors in the spatial, phase, and spectral domain caused by optical distortion.
Comparison of material measures for the determination of transfer characteristics of surface topography measuring instruments

The description of the lateral resolution criterion for profile and areal surface topography measuring instruments is still under discussion in the international standardization. Several studies have examined the different methods proposed in the standardization individually. We describe a comprehensive examination and comparison of the different methods proposed by the standard. In doing so, the star-shaped grooves material measure (type ASG of ISO 25178-70) and the chirped standard are compared by using different measuring instruments and principles. In order to benchmark the results, they are compared to a third method for the transfer function determination which uses an Rk-calibrated material measure and is based on time series modeling. The results illustrate that both described methods have their specific advantages and disadvantages and are well-suited for individual fields of application.
Robust Line Matching for Image Sequences Based on Point Correspondences and Line Mapping

This paper proposes a line segment matching method by performing line mapping and unmapping based on point correspondences. The goal of this paper is to improve the accuracy and the robustness of line segment matching for two views, which will be conducive for generating a full single-line structure for image sequences. In this paper, to improve the quantity and quality of line matches, the topological adjacency of a point-line is first introduced for two goals: to find and filter the candidate line segment by resorting to KD-tree data-index structure efficiently in the corresponding image and to leverage the candidate line segment to improve the performance of planar homography. In addition, the shift threshold parameter is theoretically analyzed, and trials are validated to determine the matching degree. Line mapping and unmapping are then used for image sequences to reduce missing matches. The extensive experimental results validate and demonstrate that our method is both more accurate and robust than existing line matching methods for two views under the circumstance of a higher recall rate. In addition, our method contributes in finding full line segments for image sequences using line mapping, with higher completeness of the 3D line model than that obtained by the state-of-the-art methods.
XOR-Counts and Lightweight Multiplication with Fixed Elements in Binary Finite Fields

XOR-metrics measure the efficiency of certain arithmetic operations in binary finite fields. We prove some new results about two different XOR-metrics that have been used in the past. In particular, we disprove a conjecture from [10]. We consider implementations of multiplication with one fixed element in a binary finite field. Here we achieve a complete characterization of all elements whose multiplication matrix can be implemented using exactly 2 XOR-operations, confirming a conjecture from [2]. Further, we provide new results and examples in more general cases, showing that significant improvements in implementations are possible.
Probabilistic Resumable Quantum Teleportation of a Two-Qubit Entangled State

We explicitly present a generalized quantum teleportation of a two-qubit entangled state protocol, which uses two pairs of partially entangled particles as quantum channel. We verify that the optimal probability of successful teleportation is determined by the smallest superposition coefficient of these partially entangled particles. However, the two-qubit entangled state to be teleported will be destroyed if teleportation fails. To solve this problem, we show a more sophisticated probabilistic resumable quantum teleportation scheme of a two-qubit entangled state, where the state to be teleported can be recovered by the sender when teleportation fails. Thus the information of the unknown state is retained during the process. Accordingly, we can repeat the teleportion process as many times as one has available quantum channels. Therefore, the quantum channels with weak entanglement can also be used to teleport unknown two-qubit entangled states successfully with a high number of repetitions, and for channels with strong entanglement only a small number of repetitions are required to guarantee successful teleportation.
Security Assessment of Blockchains in Heterogenous IoT Networks : Invited Presentation

As Blockchain technology become more understood in recent years and its capability to solve enterprise business use cases become evident, technologist have been exploring Blockchain technology to solve use cases that have been daunting industries for years. Unlike existing technologies, one of the key features of blockchain technology is its unparalleled capability to provide, traceability, accountability and immutable records that can be accessed at any point in time. One application area of interest for blockchain is securing heterogenous networks. This paper explores the security challenges in a heterogonous network of IoT devices and whether blockchain can be a viable solution. Using an experimental approach, we explore the possibility of using blockchain technology to secure IoT devices, validate IoT device transactions, and establish a chain of trust to secure an IoT device mesh network, as well as investigate the plausibility of using immutable transactions for forensic analysis.
Micro-segmentation: securing complex cloud environments

The datacentre as we know it has undergone a radical transformation in recent years – to the point where the term ‘centre’ is probably a misnomer. Enterprises of all sizes and across all sectors are rapidly migrating their datacentre operations to DevOps-based, hybrid-cloud environments, typically involving some combination of private cloud, public cloud and on-premise facilities. The datacentre as we know it has undergone a radical transformation. Mission-critical data, applications and workloads are constantly moving among the various installations as traffic levels and processing demands dictate. This agility is what makes these dynamic infrastructures attractive to organisations looking for efficiencies and competitive advantage. It is also what makes them scary from a security perspective. Micro-segmentation is an effective way to secure valuable datacentre assets and implement a zero trust model in a hybrid or multi-cloud environment, says Dave Klein of GuardiCore.