Reviewerly/exHarmony huggingface dataset:
Compressing CNNs Using Multilevel Filter Pruning for the Edge Nodes of Multimedia Internet of Things

Multimedia Internet-of-Things (IoT) systems have been widely utilized in various computer vision tasks and significantly integrated computer vision and networking capabilities. In these systems, convolutional neural networks (CNNs) perform a preliminary analysis of the collected video or image information in the edge devices. However, the high computational cost and huge storage consumption of the complex CNNs prevent their deployment on mobile-edge devices that have limited computational resource and memory. In this article, we aim to simultaneously accelerate and compress CNNs via a multilevel filter pruning (MFP) algorithm, to alleviate the dependence on the hardware of IoT edge nodes. First, a global pruning sensitivity order is defined, which could guide us to perform preliminary pruning from the perspective of convolutional layers' sensitivity. Then, the functional index of each filter is judged by the image entropy of its output feature map, which contributes to further pruning from the perspective of filter function importance. Finally, the moderate fine tuning is adopted to recover the network capability. The experimental results show that the proposed MFP algorithm could reduce 54.5% floating-point operations and 31.9% graphics memory for VGG-16 on CIFAR-10, and achieve 5.45 × floating-point acceleration and 19.70 × storage reduction for VGG-16 on ImageNet. In the reconstruction phase, the algorithm could recover the network capability much faster than the existing pruning algorithms.
Motivating data contributions via a distinct career currency

If collecting research data is perceived as poorly rewarded compared to data synthesis and analysis, this can slow overall research progress via two effects. People who have already collected data may be slow to make it openly accessible. Also, researchers may reallocate effort from collecting fresh data to synthesizing and analysing data already accessible. Here, we advocate for a second career currency in the form of data contributions statements embedded within applications for jobs, promotions and research grants. This workable step forward would provide for peer opinion to operate across thousands of selection and promotion committees and granting panels. In this way, fair valuation of data contributions relative to publications could emerge.
Cutoff for Exact Recovery of Gaussian Mixture Models

We determine the information-theoretic cutoff value on separation of cluster centers for exact recovery of cluster labels in a K-component Gaussian mixture model with equal cluster sizes. Moreover, we show that a semidefinite programming (SDP) relaxation of the K-means clustering method achieves such sharp threshold for exact recovery without assuming the symmetry of cluster centers.
Towards an understanding of online visual aesthetics: An instantiation of the composition perspective

This article articulates the importance of developing a Theory of Online Visual Aesthetics in the IS tradition, and demonstrates that to date the field is still lacking a theoretical model for explaining online visual aesthetics. To address this gap in the literature the authors propose the Online Visual Aesthetics Theoretical Model, drawn from over 100 years of literature in complementary disciplines. The Online Visual Aesthetics Theory Model proposes that the perception of the composition of an online medium is directly influenced by the eight web design categories: Graphics, Text, Simplicity, Animation, Layout, Unity, Emphasis, and Balance. The variance of each of these web design categories may then be explained by the more traditional seven primary design dimensions: Lines, Shapes, Colors, Textures, Forms, Values, and Spaces. The final instantiated Online Visual Aesthetics Theoretical Model is then presented and a program of study is articulated for the evaluation and validation of the proposed theoretical model.
Real-Time Decision Making and Path Planning for Robotic Autonomous Luggage Trolley Collection at Airports

In this article, a two-level planner is proposed to provide a solution to the autonomous luggage trolley collection problem at the airport. In the higher level planner, a decision-making problem is tackled where a sequence of luggage trolleys is determined with which the robot can collect them one by one. Based on the traditional traveling salesman problem (TSP), this decision-making problem is formulated as an open dynamic traveling salesman problem with fixed start (ODTSP-FS). Incorporating the modified transition rule, elitist global update rule, and additional local update rule, an efficient algorithm is proposed to handle this decision-making problem. The experimental results demonstrate that the proposed algorithm achieves fast convergence and smaller cost compared with the state-of-the-art algorithms. In the lower level planner, based on the pipeline of rapid-exploring random tree (RRT) scheme, a novel real-time path planning algorithm is introduced, which can adjust itself to moving obstacles and moving targets by retaining the whole tree and using two rewiring strategies. Finally, the proposed two-level planner is evaluated in a simulation environment similar to the airport to validate the effectiveness and efficiency of the proposed algorithm.
CRC-Based Error Detection Constructions for FLT and ITA Finite Field Inversions Over GF(2<sup> <i>m</i> </sup>)

Binary extension finite fields GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) have received prominent attention in the literature due to their application in many modern public-key cryptosystems and error-correcting codes. In particular, the inversion over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) is crucial for current and postquantum cryptographic applications. Schemes such as Fermat's little theorem (FLT) and the Itoh-Tsujii algorithm (ITA) have been studied to achieve better performance; however, this arithmetic operation is a complex, expensive, and time-consuming task that may require thousands of gates, increasing its vulnerability chance to natural defects. In this work, we propose efficient hardware architectures based on cyclic redundancy check (CRC) as error detection schemes for state-of-the-art finite field inversion over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) for a polynomial basis. To verify the derivations of the formulations, software implementations are performed. Likewise, hardware implementations of the original finite field inversions with the proposed error detection schemes are performed over Xilinx field-programmable gate array (FPGA) verifying that the proposed schemes achieve high error coverage with acceptable overhead.
Path to European quantum unicorns

Abstract Quantum computing holds the potential to deliver great economic prosperity to the European Union (EU). However, the creation of successful business in the field is challenging owing to the required extensive investments into postdoctoral-level workforce and sophisticated infrastructure without an existing market that can financially support these operations. This commentary paper reviews the recent efforts taken in the EU to foster the quantum-computing ecosystem together with its current status. Importantly, we propose concrete actions for the EU to take to enable future growth of this field towards the desired goals. In particular, we suggest ways to enable the creation of EU-based quantum-computing unicorns which may act as key crystallization points of quantum technology and its commercialization. These unicorns may provide stability to the otherwise scattered ecosystem, thus pushing forward global policies enabling the global spread of EU innovations and technologies. The unicorns may act as a conduit, through which the EU-based quantum ecosystem can stand out from similar ecosystems based in Asia and the United States. Such strong companies are required because of the level of investment currently required in the marketplace. This paper suggests methodologies and best practices that can enhance the probability of the creation of the unicorns. Furthermore, we explore future scenarios, in which the unicorns can operate from the EU and to support the EU quantum ecosystem. This exploration is conducted focusing on the steps to be taken and on the impact the companies may have in our opinion.
Unsupervised stereoscopic image retargeting via view synthesis and stereo cycle consistency losses

Stereoscopic image retargeting aims to manipulate the stereoscopic images to fit various devices with different resolutions and prescribed aspect ratios. With the development of various types of three-dimensional (3D) displays, stereoscopic image retargeting becomes increasingly popular in the field of computer graphics. In this paper, we propose an unsupervised stereoscopic image retargeting network (USIR-Net) to address the problem of stereoscopic image retargeting without label information. By exploring the inter-view correlation and disparity relationship of stereoscopic images, two unsupervised losses are developed to guide the learning of stereoscopic image retargeting model. First, in view of the inter-view correlation, a view synthesis loss is proposed to guarantee the generation of high-quality stereoscopic images with accurate inter-view relationship. Second, by exploiting the consistency of stereoscopic images before and after the retargeting, a stereo cycle consistency loss, which consists of a content consistency term and a disparity consistency term, is developed to preserve the structure information and prevent binocular disparity inconsistency. Quantitative and qualitative experimental results demonstrate that the proposed method achieves superior performance compared with state-of-the-art methods.
Knowledge Graph Question Answering Using Graph-Pattern Isomorphism

Knowledge Graph Question Answering (KGQA) systems are often based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges.
Evaluating Commit, Issue and Product Quality in Team Software Development Projects

Providing students with authentic software development experiences is essential to preparing them for careers in industry. To that end, many undergraduate courses include a team-based software development experience in which each team works on a different software project. This raises significant challenges for assessing student work and measuring the impact of pedagogical interventions: What do we measure and how, when each team is working on a different project? To address this question, we present a collection of metrics developed using the Goal-Question-Metric framework from the empirical software engineering literature, and an empirical study in which we applied those metrics to assess 23 team software projects involving 94 students at three institutions. Study results suggest that these metrics, which gauge commit, issue, and overall product quality, are sensitive to differences in the quality of teams' processes and products. This work contributes a new metric-based approach to evaluating key aspects of software development processes and products in a wide variety of computing courses.
Carrier Leakage Estimation Method for Cross-Receiver Specific Emitter Identification

Specific emitter identification (SEI) technology uses fingerprint features originated from the emitter imperfections to identify the transmitting devices. However, the fingerprint features used in SEI are subtle and can be easily affected by the distortion of the receivers. This means that the features cannot be used universally among different receivers, which can bring many limits in identification but is rarely concerned in previous studies. To resolve this problem, the effects of receiver distortions on I/Q modulator fingerprints are discussed, and a carrier leakage estimation method for cross-receiver SEI is developed. In the proposed method, joint estimation of carrier leakage and filter distortions is included to eliminate the influence of the receiver. In the simulation and experiment, the performance of the proposed method and the traditional method is compared. The results validate that the proposed method can effectively reduce the feature offset caused by receiver distortions and the proposed method has the same effectiveness as the traditional one. The proposed method can be widely used in cross-receiver specific emitter identification.
Inference on tissue transplantation experiments

We review studies on tissue transplantation experiments for various species: one piece of the donor tissue is excised and transplanted into a slit in the host tissue, then observe the behavior of this grafted tissue. Although we have known the results of some transplantation experiments, there are many more possible experiments with unknown results. We develop a penalty function-based method that uses the known experimental results to infer the unknown experimental results. Similar experiments without similar results get penalized and correspond to smaller probability. This method can provide the most probable results of a group of experiments or the probability of a specific result for each experiment. This method is also generalized to other situations. Besides, we solve a problem: how to design experiments so that such a method can be applied most efficiently.
Design and analysis of gantry robot for pick and place mechanism with Arduino Mega 2560 microcontroller and processed using pythons

Robots are extensively used in industries for their precision work and amount of work that one can obtain without any defects. In this paper we are using a gantry robot for as it does not occupy the floor space therefore reducing the distance for reachability of the parts and hence reducing unnecessary material for guide way. Robots work in strictly defined path and there is no or very little change in such systems in order to overcome this we are using a vision based control system to make the system dynamic in nature the images are picked by using a USB camera processed images of the object is transmitted via serial communication to the Arduino Mega 2560 microcontroller and processed using pythons open source computer vision (Open CV) image to process the image captured by the USB camera to find the exact colour and to pick the object and sort it.
Real‐time classification on oral ulcer images with residual network and image enhancement

With the advances of deep learning research in the past few years, healthcare and smart medicines have been significantly developed. Inspired by the wide application of deep learning in medical image classification and disease diagnosis, this paper further proposes a variant of the Residual Network framework to classify the oral ulcer images in real-time. In particular, image pre-processing and enhancement techniques are used to enrich the datasets and reduce model overfitting. Besides, the transfer learning is further introduced into the residual blocks to improve the classification accuracy, with the later layers trained from the labeled datasets. To validate the performance of authors' proposal, it is compared with other classic deep learning models with respect to the classification sensitivity, specificity, and accuracy. The experimental results show that authors' approach outperforms those classic classification networks when the oral ulcers are classified and diagnosed in real-time.
Underwater Object Detection Based on Improved Single Shot MultiBox Detector

Underwater optical images are scarce, and there are varying degrees of blur and color distortion, which brings great challenges to the detection of underwater objects. In view of the shortcomings of the original Single Shot MultiBox Detector (SSD), in this paper, a shallow object detection layer is added to the original SSD model to improve the network's ability to detect small objects. At the same time, this article improves the confidence loss to narrow the ability of SSD to detect different types of objects. Using the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm to process the original images, enhance the feature information of the objects in the underwater images. Training the improved SSD network through transfer learning to overcome the limitations of insufficient underwater images. Experimental results show that the algorithm proposed in this paper has better detection performance than the original SSD, YOLO v3 and other algorithms, which is of great significance to the realization of underwater object detection.
Adaptive DNN Partition in Edge Computing Environments

Deep Neural Network (DNN) has been applied widely nowadays, making remarkable achievements in a wide variety of research fields. With the improvement of the accuracy requirements for the inference results, the topology of DNN tends to be more and more complex, evolving from chain topology to directed acyclic graph (DAG) topology, which leads to the huge amount of computation. For those end devices which have limited computing resources, the delay of running DNN models independently may be intolerable. As a solution, edge computing can make use of all available devices in the edge computing environments comprehensively to run DNN inference tasks, so as to achieve the purpose of acceleration. In this case, how to split DNN inference task into several small tasks and assign them to different edge devices is the central issue. This paper proposes a load-balancing algorithm to split DNN with DAG topology adaptively according to the environment. Extensive experimental results show the the propose adaptive algorithm can effectively accelerate the inference speed.
Effects of losses on the sensitivity of an actively correlated Mach-Zehnder interferometer

An actively correlated Mach-Zehnder interferometer (ACMZI) renders a number of significant advantages over a conventional Mach-Zehnder interferometer (MZI) with a squeezed vacuum state input such as an active output readout, loss tolerant detection, and multiarms for multiparameter estimation. We theoretically studied the quantum Cram\'er-Rao bound of an ACMZI, where the quantum Fisher information obtained by the phase-averaging method can give the conclusive phase-sensing sensitivity without any external phase reference. We numerically calculate the phase sensitivities with the method of homodyne detection and intensity detection in the presence of losses. Under lossless and very low loss conditions, the ACMZI is operated in a balanced case to beat the standard quantum limit (SQL). As the loss increases, the reduction in sensitivity increases. Fortunately, within a certain range, we can adjust the gain parameters of the beam recombination process to reduce the reduction in sensitivity and realize the sensitivity can continue to beat the SQL in an unbalanced situation. Our scheme provides an optimization method of phase estimation in the presence of losses.
DTNB: A Blockchain Transaction Framework With Discrete Token Negotiation for the Delay Tolerant Network

The current blockchain deployment solutions rely on a continuous connectivity network. Unfortunately, the delay tolerant network does not meet this condition. Therefore, we construct a novel blockchain transaction framework with discrete token negotiation called DTNB, which can be deployed on the delay tolerant network to provide transaction services. Specifically, we present the structure of add-chains by modifying the existing block structure. Then, we design a mining qualification determining scheme to achieve fair transactions, including discrete token generation algorithm and mining qualification attribution algorithm, and this scheme avoids the problem that nodes with more stakes in the PoS and DPoS algorithms can obtain mining qualifications with a higher probability. Furthermore, we present two mining schemes and also design a fork processing algorithm, which ensures that blocks on the add-chain generated by the local network can be appended to the main chain with the equal probability. Finally, we design the second consensus algorithm to avoid the problem of false and repeated transactions of the blocks on the add-chains in the local network. Theoretical analysis shows three properties of DTNB, including safety, reliability and activeness, and the experimental simulations demonstrate DTNB has advantages in throughput, block generation time and fork rate.
Mixed Reality: A Reconsideration Based on Mixed Objects and Geospatial Modalities

We put forward a conceptualization of Mixed Reality as a blend of digital objects with real ones that coexist and interact with each other and they are also spatially referenced so that they are properly perceived in space by an observer that could potentially be at any position any time. In accordance with this statement, we have adopted the concept of a Mixed Object which is composed of a set of physical properties linked with a set of digital ones. In our case, the physical properties are acquired by employing geospatial technologies such as photogrammetry, laser scanning, unmanned aerial vehicles and positioning systems and are further processed in order to be visually displayed by utilizing Geographical Information Systems and Geovisualization frameworks in combination with traditional image processing techniques. We show that the Mixed Object approach is in conformance with Microsoft’s approach on Mixed Reality as the common space between humans, computers, and the environment and we further incorporate in these the Geospatial Linking Modalities. We finally provide an affordable MR experience as a proof of concept, by utilizing a smartphone for capturing and visualizing the environment, a visual tag for positioning purposes and freely available photogrammetrically mapped content and virtual objects to form a digital scene blended with the real environment.