Reviewerly/exHarmony huggingface dataset:
NetScore: Towards Universal Metrics for Large-Scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage

Much of the focus in the design of deep neural networks has been on improving accuracy, leading to more powerful yet highly complex network architectures that are difficult to deploy in practical scenarios, particularly on edge devices such as mobile and other consumer devices given their high computational and memory requirements. As a result, there has been a recent interest in the design of quantitative metrics for evaluating deep neural networks that accounts for more than just model accuracy as the sole indicator of network performance. In this study, we continue the conversation towards universal metrics for evaluating the performance of deep neural networks for practical on-device edge usage. In particular, we propose a new balanced metric called NetScore, which is designed specifically to provide a quantitative assessment of the balance between accuracy, computational complexity, and network architecture complexity of a deep neural network, which is important for on-device edge operation. In what is one of the largest comparative analysis between deep neural networks in literature, the NetScore metric, the top-1 accuracy metric, and the popular information density metric were compared across a diverse set of 60 different deep convolutional neural networks for image classification on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) dataset. The evaluation results across these three metrics for this diverse set of networks are presented in this study to act as a reference guide for practitioners in the field. The proposed NetScore metric, along with the other tested metrics, are by no means perfect, but the hope is to push the conversation towards better universal metrics for evaluating deep neural networks for use in practical on-device edge scenarios to help guide practitioners in model design for such scenarios.
Software Product Line Engineering

The lack of mature tool support is one of the main reasons that make the industry to be reluctant to adopt Software Product Line (SPL) approaches. A number of systematic literature reviews exist that identify the main characteristics offered by existing tools and the SPL phases in which they can be applied. However, these reviews do not really help to understand if those tools are offering what is really needed to apply SPLs to complex projects. These studies are mainly based on information extracted from the tool documentation or published papers. In this paper, we follow a different approach, in which we firstly identify those characteristics that are currently essential for the development of an SPL, and secondly analyze whether the tools provide or not support for those characteristics. We focus on those tools that satisfy certain selection criteria (e.g., they can be downloaded and are ready to be used). The paper presents a state of practice with the availability and usability of the existing tools for SPL, and defines different roadmaps that allow carrying out a complete SPL process with the existing tool support.
Domain-Adaptive Pedestrian Detection in Thermal Images

This paper presents an approach to pedestrian detection in thermal infrared (thermal) images with limited annotations. The key idea is to adapt the abundance of color images associated with bounding box annotations to the thermal domain for training the pedestrian detector. To this end, we couple a domain adaptation component that consists of a pair of image transformers with a pedestrian detector in the thermal domain and train the entire network end-to-end. The image transformers act as a data augmentation tool that progressively improves synthetic examples on the fly for training the pedestrian detector. To aid the training process, we introduce a detection loss defined on both real thermal images and synthetic thermal images transformed from the color domain. The proposed detector outperforms existing methods on the thermal images from the KAIST detection benchmark [1].
An efficient denoising of impulse noise from MRI using adaptive switching modified decision based unsymmetric trimmed median filter

Noise reduction in digital images is the eminent preprocessing scheme in major image processing activities which includes medical image segmentation. A well designed noise reduction scheme for highly corrupted medical images is needed for medical diagnosing schemes with an immediate attention. This paper proposes a novel Adaptive Switching Modified Decision Based Unsymmetric Trimmed Median Filter (ASMDBUTMF) for noise reduction in gray scale MR Images which are affected by salt and pepper noise. The Proposed method is the extension work of the existing Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) method. In this paper noisy pixels are detected using the occurrence of intensity values 0’s and 255’s. The noisy pixels are converted into noise-free pixels using trimmed median value s. The MDBUTMF Filter uses 3x3 size windows only while the proposed method uses window sizes based on Adaptive switching method to retain more details for noise reduction. The proposed ASMDBUTMF algorithm is compared with existing methods, namely Median Filter (MF), Rank Order Median Filter (ROMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) and Geometric Multiscale Ridgelet Support Vector Transform Filter (GMRSVTF) simultaneously. The new ASMDBUTMF Filter supports noise reduction for highly corrupted images with better peak signal-to-noise Ratio (PSNR) and Image Enhancement Factor (IEF).
A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas.As a powerful optimization tool for many real-world applications, evolutionary algorithms (EAs) fail to solve the emerging large-scale problems both effectively and computationally efficiently.In this paper, we propose a novel Divide-and-Conquer (DC) based EA that can not only produce high-quality solutions by solving sub-problems separately, but also benefits significantly from the power of parallel computing by solving the sub-problems simultaneously.Existing DC-based EAs that were thought to enjoy the same advantages of the proposed algorithm, are shown to be practically incompatible with the parallel computing scheme, unless some trade-offs are made by compromising the solution quality.
F-E3D: FPGA-based Acceleration of an Efficient 3D Convolutional Neural Network for Human Action Recognition

Three-dimensional convolutional neural networks (3D CNNs) have demonstrated their outstanding classification accuracy for human action recognition (HAR). However, the large number of computations and parameters in 3D CNNs limits their deployability in real-life applications. To address this challenge, this paper adopts an algorithm-hardware co-design method by proposing an efficient 3D CNN building unit called 3D-1 bottleneck residual block (3D-1 BRB) at the algorithm level, and a corresponding FPGA-based hardware architecture called F-E3D at the hardware level. Based on 3D-1 BRB, a novel 3D CNN model called E3DNet is developed, which achieves nearly 37 times reduction in model size and 5% improvement in accuracy compared to standard 3D CNNs on the UCF101 dataset. Together with several hardware optimizations, including 3D fused BRB, online blocking and kernel reuse, the proposed F-E3D is nearly 13 times faster than a previous FPGA design for 3D CNNs, with performance and accuracy comparable to other state-of-the-art 3D CNN models on GPU platforms while requiring only 7% of their energy consumption.
The potential of forecasting in reducing the LCOE in PV plants under ramp-rate restrictions

An increasing number of grid codes are requiring the limitation of the PV output power fluctuation over a given time scale. Batteries represent the most obvious solution to smooth power fluctuations, with the corresponding negative impact on the PV energy cost. However, short-term forecasting is currently being proposed as a tool to reduce battery capacity requirements or even completely remove it. Although these solutions decrease or avoid the battery cost, it also entails some energy curtailment losses which obviously raise the final cost of PV energy. This energy losses, currently unknown, are independent of the forecasting accuracy and represent the minimal additional cost in the hypothetical case of a perfect prediction. Thus, this paper compares Levelized Cost of Energy (LCOE) of three ramp-rate control strategies in order to determine which would give the lowest cost: battery-based, ideal short-term forecasting, or a combination of both. Results show that curtailment losses would be small enough to make battery-less strategy an appropriate choice, so it is worthwhile improving short-term forecasting in view of the potential LCOE savings. Database is taken from high resolution measurements recorded for over a year at 8 PV plants ranging from 1 to 46 MWp.
Cityscape protection using VR and eye tracking technology

The traditional method for reconstructing cityscape relies greatly on the subjective judgment of designers, which makes the cityscape simple and homogenized. This paper aims to propose a new integrated approach to protect and design cityscape based on virtual reality (VR) and eye tracking technology. Through the integration and quantification of the eye tracking data and the protocol analysis data in the VR environment, this research has revealed the mechanism of identifying the cityscape features, and discovered the differences in the perception of the cityscape features by different people, thus proposing the multi-cultural integrated strategy for protecting cityscape. This research is of great significance for building a human-oriented scientific planning and protection method and promoting the application of cutting-edge digital technology in the field of smart city governance.
Fine-grained action segmentation using the semi-supervised action GAN

In this paper we address the problem of continuous fine-grained action segmentation, in which multiple actions are present in an unsegmented video stream. The challenge for this task lies in the need to represent the hierarchical nature of the actions and to detect the transitions between actions, allowing us to localise the actions within the video effectively. We propose a novel recurrent semi-supervised Generative Adversarial Network (GAN) model for continuous fine-grained human action segmentation. Temporal context information is captured via a novel Gated Context Extractor (GCE) module, composed of gated attention units, that directs the queued context information through the generator model, for enhanced action segmentation. The GAN is made to learn features in a semi-supervised manner, enabling the model to perform action classification jointly with the standard, unsupervised, GAN learning procedure. We perform extensive evaluations on different architectural variants to demonstrate the importance of the proposed network architecture, and show that it is capable of outperforming current state-of-the-art on three challenging datasets: 50 Salads, MERL Shopping and Georgia Tech Egocentric Activities dataset.
Feature construction for fraudulent credit card cash-out detection

As a type of credit card fraud behavior, fraudulent cash-out causes banks and companies to incur huge losses. Furthermore, such organizations lack effective methods to detect fraudulent cash-out. To detect fraudulent cash-out accurately, we construct four feature sets based on domain knowledge from industry experts, tips from fraudsters, related media reports, and the relevant literature. In addition, we construct features using a functional data analysis algorithm to capture the time-dependent behavioral patterns of cardholders. We also construct a benchmark feature set based on the traditional approach of Whitrow's strategy. We compare these feature sets using a real data set comprising real transactions of 25,000 credit cards with three machine learning methods: eXtreme gradient boosting, random forest, and support vector machine. The results reveal that our proposed features, which consider both snapshot and dynamic behavioral patterns of cardholders, achieve considerably superior performance to that of Whitrow's strategy. The precisions at the top 5%, 10%, 15%, and 20% levels are improved by magnitudes of 0.049, 0.081, 0.053, and 0.046, respectively.
Hybrid‐based novel approach for resource scheduling using MCFCM and PSO in cloud computing environment

Summary Cloud computing is a growing environment. Many of the users are interested to outsource their data in cloud; however, load balancing in cloud is still at risk. Resource allocation plays a major role in load balancing. In this scheduling problem, independent task in cloud computing can allocate resource by the summary of modified canopy fuzzy c‐means algorithm (MCFCMA). To allocate task to their corresponding resource, particle swarm‐based optimization algorithm (PSO) is used. In proposed scheme, first independent task selected based on load feed‐back, cluster the requested task using MCFCMA and schedule task to each virtual machine. VM selects parallel execution in virtual machine manager. Calculate feature value using PSO algorithm. Allocate resource to the task. Since our proposed system selects resource based on parallel execution, it reduces load balancing in Cloud Quantum Computation (CQC). The proposed system overcomes issues in load balancing and load scheduling; this can be proved by its precision and privacy calculation.
RecSys challenge 2019

The workshop features presentations of accepted contributions to the RecSys Challenge 2019 organized by trivago, TU Wien, Politecnico di Bari, and Karlsruhe Institute of Technology. In the challenge, which originates from the domain of online travel recommender systems, participants had to build a click-prediction model based on user session interactions. Predictions were submitted in the form of a list of suggested accommodations and evaluated on an offline data set that contained the information what accommodation was clicked in the later part of a session. The data set contains anonymized information about almost 16 million session interactions of over 700.000 users visiting the trivago website.
Towards Efficient Verifiable Forward Secure Searchable Symmetric Encryption

Searchable Symmetric Encryption (SSE) allows a server to perform search directly over encrypted data outsourced by user. Recently, the primitive of forward secure SSE has attracted significant attention due to its favorable property for dynamic data searching. That is, it can prevent the linkability from newly update data to previously searched keyword. However, the server is assumed to be honest-but-curious in the existing work. How to achieve verifiable forward secure SSE in malicious server model remains a challenging problem. In this paper, we propose an efficient verifiable forward secure SSE scheme, which can simultaneously achieve verifiability of search result and forward security property. In particular, we propose a new verifiable data structure based on the primitive of multiset hash functions, which enables efficient verifiable data update by incrementally hash operation. Compared with the state-of-the-art solution, our proposed scheme is superior in search and update efficiency while providing verifiability of search result. Finally, we present a formal security analysis and implement our scheme, which demonstrates that our proposed scheme is equipped with the desired security properties with practical efficiency.
State of the art in information security policy development

Despite the prevalence of research that exists under the label of "information security policies" (ISPs), there is no consensus on what an ISP means or how ISPs should be developed. This article reviews state-of-the-art ISP development by examining a diverse sample of literature on the subject. The definition and function of an ISP is studied first, revealing a rich tapestry of different notions behind the same term. When looking at the broad picture of the research on ISP development methods, we find different phases and levels of detail. Analyzing the different views on the content, context, and strategy alignment provides for further understanding on the complexity of the matter. As an outcome, we raise issues in ISP definitions and development methods that should be addressed in future research and practical applications. This review concludes that for state-of-the-art ISP development, the focus should shift more toward organization-specific information security needs, as the direction of the current research is still lacking contributions that would show how contextual factors could be successfully integrated into ISP development.
Semantic Component Decomposition for Face Attribute Manipulation

Deep neural network-based methods were proposed for face attribute manipulation. There still exist, however, two major issues, i.e., insufficient visual quality (or resolution) of the results and lack of user control. They limit the applicability of existing methods since users may have different editing preference on facial attributes. In this paper, we address these issues by proposing a semantic component model. The model decomposes a facial attribute into multiple semantic components, each corresponds to a specific face region. This not only allows for user control of edit strength on different parts based on their preference, but also makes it effective to remove unwanted edit effect. Further, each semantic component is composed of two fundamental elements, which determine the edit effect and region respectively. This property provides fine interactive control. As shown in experiments, our model not only produces high-quality results, but also allows effective user interaction.
An Embedding-Based Approach to Rule Learning in Knowledge Graphs

It is natural and effective to use rules for representing explicit knowledge in knowledge graphs. However, it is challenging to learn rules automatically from very large knowledge graphs such as Freebase and YAGO. This paper presents a new approach, RLvLR (Rule Learning via Learning Representations), to learning rules from large knowledge graphs by using the technique of embedding in representation learning together with a new sampling method. Based on RLvLR, a new method RLvLR-Stream is developed for learning rules from streams of knowledge graphs. Both RLvLR and RLvLR-Stream have been implemented and experiments conducted to validate the proposed methods regarding the tasks of rule learning and link prediction. Experimental results show that our systems are able to handle the task of rule learning from large knowledge graphs with high accuracy and outperform some state-of-the-art systems. Specifically, for massive knowledge graphs with hundreds of predicates and over 10M facts, RLvLR is much faster and can learn much more quality rules than major systems for rule learning in knowledge graphs such as AMIE+. In the setting of knowledge graph streams, RLvLR-Stream significantly improved RLvLR for both rule learning and link prediction.
Josephson Array-Mode Parametric Amplifier

We introduce a novel near-quantum-limited amplifier with a large tunable bandwidth and high dynamic range - the Josephson Array Mode Parametric Amplifier (JAMPA). The signal and idler modes involved in the amplification process are realized by the array modes of a chain of 1000 flux tunable, Josephson-junction-based, nonlinear elements. The frequency spacing between array modes is comparable to the flux tunability of the modes, ensuring that any desired frequency can be occupied by a resonant mode, which can further be pumped to produce high gain. We experimentally demonstrate that the device can be operated as a nearly quantum-limited parametric amplifier with 20 dB of gain at almost any frequency within (4-12) GHz band. On average, it has a 3 dB bandwidth of 11 MHz and input 1 dB compression power of -108 dBm, which can go as high as -93 dBm. We envision the application of such a device to the time- and frequency-multiplexed readout of multiple qubits, as well as to the generation of continuous-variable cluster states.
Virtual Reality for Design and Construction Education Environment

In the last few years, the application of VR has exponentially grown to give visually large field-of-view from planning to execution stages of any types of civil infrastructure systems. Handling the complexity and uncertainty of construction projects, however, requires well-prepared architects and engineers. Architecture and civil engineering students are also expected to go through the proper training to be well-equipped for this challenge. VR has proved a helpful tool for architecture, engineering, and construction (AEC) education environments with the aim to leave a lasting impact on a student’s learning and experience. The purpose of this paper is to provide a brief review of the most recent VR systems in the design and construction industry and the education environment. The paper also evaluates the use of VR in a two-pronged approach by conducting a comprehensive literature review and interviewing students in the master of project management (MPM) program. This paper could provide a roadmap for future efforts to implement VR into the architectural and construction industry as well as the education environment.
Robust Integrated Optical Unitary Converter Using Multiport Directional Couplers

An integrated optical unitary converter (OUC), which can realize arbitrary N × N unitary transformation on chip, is promising for widespread applications in various areas, such as optical communication, quantum information processing, and optical neural networks. Most of the integrated OUCs demonstrated to date comprise cascaded 2 × 2 Mach-Zehnder interferometers (MZIs) based on Reck's scheme or its variation, or cascaded multimode interference (MMI) couplers based on multi-plane light conversion (MPLC). However, these schemes are sensitive to fabrication errors, making it difficult to realize high-performance large-scale OUCs. In this work, we experimentally demonstrate a silicon photonic 4 × 4 OUC that is inherently robust to fabrication errors, based on a novel structure. The proposed structure consists of cascaded multiport directional couplers (DCs) and phase shifter arrays. In this scheme, the multiport DC assures unitary transformation with inherent robustness against fabrication errors. By optimizing all the phase shifters using a simulated annealing algorithm, we experimentally demonstrate reconfigurable 4-mode demultiplexing and error-free demultiplexing of 40-Gb/s non-return-to-zero (NRZ) signals.