Reviewerly/exHarmony huggingface dataset:
Rent’s rule and extensibility in quantum computing

Quantum computing is on the verge of a transition from fundamental research to practical applications. Yet, to make the step to large-scale quantum computation, an extensible qubit system has to be developed. In classical semiconductor technology, this was made possible by the invention of the integrated circuit, which allowed to interconnect large numbers of components without having to solder to each and every one of them. Similarly, we expect that the scaling of interconnections and control lines with the number of qubits will be a central bottleneck in creating large-scale quantum technology. Here, we define the quantum Rent exponent p to quantify the progress in overcoming this challenge at different levels throughout the quantum computing stack. We further discuss the concept of quantum extensibility as an indicator of a platform’s potential to reach the large quantum volume needed for universal quantum computing and review extensibility limits faced by different qubit implementations on the way towards truly large-scale qubit systems.
Quantum analog-digital conversion

Many quantum algorithms, such as the Harrow-Hassidim-Lloyd (HHL) algorithm, depend on oracles that efficiently encode classical data into a quantum state. The encoding of the data can be categorized into two types: analog encoding, where the data are stored as amplitudes of a state, and digital encoding, where they are stored as qubit strings. The former has been utilized to process classical data in an exponentially large space of a quantum system, whereas the latter is required to perform arithmetics on a quantum computer. Quantum algorithms such as HHL achieve quantum speedups with a sophisticated use of these two encodings. In this work, we present algorithms that convert these two encodings to one another. While quantum digital-to-analog conversions have implicitly been used in existing quantum algorithms, we reformulate it and give a generalized protocol that works probabilistically. On the other hand, we propose a deterministic algorithm that performs a quantum analog-to-digital conversion. These algorithms can be utilized to realize high-level quantum algorithms such as a nonlinear transformation of amplitudes of a quantum state. As an example, we construct a ``quantum amplitude perceptron,'' a quantum version of the neural network that hence has a possible application in the area of quantum machine learning.
Human Pose Estimation in Video via Structured Space Learning and Halfway Temporal Evaluation

Human pose estimation from image or video is a basic issue in computer graphics and computer vision. The challenge of human pose estimation in video lies in the temporal coherency issue. The temporal consistency in video is the contents' similarity shown in the video frames. In video, temporal consistency maintenance of human pose estimation is to obtain better long-term consistency. Great major methods for the long-term consistency are using the whole video optimization method, which makes very large computation and the absence of consistency before and after the articulated limbs. In this paper, a novel method for the maintenance of temporal consistency is proposed. We maintain the temporal consistency of the video by the structured space learning and halfway temporal evaluation methods. We adopt a three-stage multi-feature deep convolution network framework to generate the initial posture joints position data, and a long-term temporal coherence is propagated to the overall video at each stage. The long-term consistency is more appealing since it produces stable results over larger periods of time. Our method can achieve good temporal consistency and get accurate and stable human pose estimation results. Various experimental results demonstrated the superiority of our method.
Packing Convolutional Neural Networks in the Frequency Domain

Deep convolutional neural networks (CNNs) are successfully used in a number of applications. However, their storage and computational requirements have largely prevented their widespread use on mobile devices. Here we present a series of approaches for compressing and speeding up CNNs in the frequency domain, which focuses not only on smaller weights but on all the weights and their underlying connections. By treating convolution filters as images, we decompose their representations in the frequency domain as common parts (i.e., cluster centers) shared by other similar filters and their individual private parts (i.e., individual residuals). A large number of low-energy frequency coefficients in both parts can be discarded to produce high compression without significantly compression romising accuracy. Furthermore, we explore a data-driven method for removing redundancies in both spatial and frequency domains, which allows us to discard more useless weights by keeping similar accuracies. After obtaining the optimal sparse CNN in the frequency domain, we relax the computational burden of convolution operations in CNNs by linearly combining the convolution responses of discrete cosine transform (DCT) bases. The compression and speed-up ratios of the proposed algorithm are thoroughly analyzed and evaluated on benchmark image datasets to demonstrate its superiority over state-of-the-art methods.
Multiple Attributes QoS Prediction via Deep Neural Model with Contexts

In recent years, various collaborative QoS prediction methods have been put forward to coping with the demand for efficient quality-of-service (QoS) evaluation, by drawing lessons from the recommender systems. However, there still remain some challenging issues on this direction, as how to effectively exploit complex contexts to improve prediction accuracy, and how to realize collaborative QoS prediction of multiple attributes. Inspired by the principles of deep learning, we have proposed a universal deep neural model (DNM) for making multiple attributes QoS prediction with contexts. In this model, contextual features are mapped into a shared latent space to semantically characterize them in the embedding layer. The contextual features with their higher-order interactions are captured through the interaction layer and the perception layers. Multi-tasks prediction is realized by stacking task-specific perception layers on the shared neural layers. Armed with these, DNM provides a powerful framework to integrate with various contextual features to realize multi-attributes QoS prediction. Experimental results from a large-scale QoS-specific dataset demonstrate that DNM achieves superior prediction accuracy in term of mean absolute error (MAE) compared with the state-of-the-art collaborative QoS prediction techniques. Additionally, the DNM model has a good robustness and extensibility on exploiting heterogeneous contextual features.
An artificial bee colony algorithm search guided by scale-free networks

Many optimization algorithms have adopted scale-free networks to improve the search ability. However, most methods have merely changed their population topologies into those of scale-free networks; their experimental results cannot verify that these algorithms have superior performance. In this paper, we propose a scale-free artificial bee colony algorithm (SFABC) in which the search is guided by a scale-free network. The mechanism enables the SFABC search to follow two rules. First, the bad food sources can learn more information from the good sources of their neighbors. Second, the information exchange among good food sources is relatively rare. To verify the effectiveness of SFABC, the algorithm is compared with the original artificial bee colony algorithm (ABC), several advanced ABC variants, and other metaheuristic algorithms on a wide range of benchmark functions. Experimental results and statistical analyses indicate that SFABC obtains a better balance between exploration and exploitation during the optimization process and that, in most cases, it can provide a competitive performance of the benchmark functions. We also verify that scale-free networks can not only improve the optimization performance of ABC but also enhance the search ability of other metaheuristic algorithms, such as differential evolution (DE) and the flower pollination algorithm (FPA).
Scaling Video Analytics Systems to Large Camera Deployments

Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying video cameras en masse for the spatial monitoring of their physical premises. Scaling video analytics to massive camera deployments, however, presents a new and mounting challenge, as compute cost grows proportionally to the number of camera feeds. This paper is driven by a simple question: can we scale video analytics in such a way that cost grows sublinearly, or even remains constant, as we deploy more cameras, while inference accuracy remains stable, or even improves. We believe the answer is yes. Our key observation is that video feeds from wide-area camera deployments demonstrate significant content correlations (e.g. to other geographically proximate feeds), both in space and over time. These spatio-temporal correlations can be harnessed to dramatically reduce the size of the inference search space, decreasing both workload and false positive rates in multi-camera video analytics. By discussing use-cases and technical challenges, we propose a roadmap for scaling video analytics to large camera networks, and outline a plan for its realization.
How Practitioners Perceive Automated Bug Report Management Techniques

Bug reports play an important role in the process of debugging and fixing bugs. To reduce the burden of bug report managers and facilitate the process of bug fixing, a great amount of software engineering research has been invested toward automated bug report management techniques. However, the verdict is still open whether such techniques are actually required and applicable outside the domain of theoretical research. To fill this gap, we conducted a survey among 327 practitioners to gain their insights into various categories of automated bug report management techniques. Specifically, we asked the respondents to rate the importance of such techniques and provide the rationale. To get deeper insights into practitioners' perspective, we conducted follow-up interviews with 25 interviewees selected from the survey respondents. Through the survey and the interviews, we gained a better understanding of the perceived usefulness (or its lack) of different categories of automated bug report management techniques. Based on our findings, we summarized some potential research directions in developing techniques to help developers better manage bug reports.
A new heuristic for multilevel thresholding of images

Multilevel thresholding of images helps in separating the interesting objects from their background. The quality of separation depends much on the selected threshold values. This paper proposes a novel thresholding (TH) heuristic for multilevel thresholding problem. Further, the proposed TH heuristic is embedded into whale optimization algorithm (WOA), grey wolf optimizer (GWO), particle swarm optimization (PSO) algorithm to develop new algorithms named WOA-TH, GWO-TH, and PSO-TH respectively. The TH heuristic fine-tunes the best solution of employer nature-inspired algorithms for increasing their capability to escape local optimum. To find the optimum thresholds for an image, between-class variance criterion is employed as the fitness function. Experiments have been performed on twenty benchmark test images using six different number of thresholds. The performance of the proposed algorithms is compared with their respective base algorithms. The results demonstrate that the proposed WOA-TH, GWO-TH, and PSO-TH algorithms are superior to the compared algorithms. Moreover, the computational time of WOA, GWO, and PSO is improved through the incorporation of proposed heuristic.
Transferred deep learning based waveform recognition for cognitive passive radar

Passive radar capable of recognizing illumination of opportunities can improve the detection performance on account of its functional properties of environment adaptivity. Waveform recognition approaches based on Deep Learning can outperform traditional methods based on hand-crafted feature as shown in recent studies. In this paper, we propose a novel transferred deep learning waveform recognition method which makes use of multi-scale convolution and temporal dependency characteristics to improve the recognition performance. Firstly, we develop a two-channel convolutional neural networks combining with Bi-directional Long Short-Term Memory (TCNN-BL) architecture to extract features of different scales and merge past and future states. Then in order to solve the transferability problem across various sampling frequencies, we present a parameter transfer approach which initializes target domain classifier using source domain parameters. Based on our experiments on both public datasets and our own datasets, it can be demonstrated that the proposed approach significantly outperforms the state-of-the-art methods.
A Truthful Reverse-Auction Mechanism for Computation Offloading in Cloud-Enabled Vehicular Network

The growth of smart vehicles and computation-intensive applications poses new challenges in providing reliable and efficient vehicular services. Offloading such applications from vehicles to mobile edge cloud servers has been considered as a remedy, although resource limitations and coverage constraints of the cloud service may still result in unsatisfactory performance. Recent studies have shown that exploiting the unused resources of nearby vehicles for application execution can augment the computational capabilities of application owners while alleviating heavy on-board workloads. However, encouraging vehicles to share resources or execute applications for others remains a sensitive issue due to user selfishness. To address this issue, we establish a novel computation offloading marketplace in vehicular networks where a Vickrey-Clarke-Groves based reverse auction mechanism utilizing integer linear programming (ILP) problem is formulated while satisfying the desirable economical properties of truthfulness and individual rationality. As ILP has high computation complexity which brings difficulties in implementation under larger and fast changing network topologies, we further develop an efficient unilateral-matching-based mechanism, which offers satisfactory suboptimal solutions with polynomial computational complexity, truthfulness and individual rationality properties as well as matching stability. Simulation results show that, as compared with baseline methods, the proposed unilateral-matching-based mechanism can greatly improve the system efficiency of vehicular networks in all traffic scenarios.
Functional and Contextual Attention-Based LSTM for Service Recommendation in Mashup Creation

Service recommendation is a fundamental task in many application environments (e.g., Mashup creation and cloud computing). In the past, various methods have been proposed to facilitate the service selection process based on the original functional descriptions. However, the mined features from the descriptions are usually too sparse for training a well-performed model. In addition, most methods neglect to differentiate the weights of various features, while words included in descriptions usually exhibit different intentions (e.g., functional or non-functional). To address these challenges, in this paper we propose a text expansion and deep model-based approach for service recommendation. Specifically, we first expand the description of services at sentence level based on a novel probabilistic topic model that learns topics of words, sentences and descriptions in a stratified fashion. The expansion process can bridge the vocabulary gap between services and user queries with the collective semantic similarity of sentences and descriptions. Then, we propose a Long Short-Term Memory-based model to recommend services with two attention mechanisms - a functional attention mechanism that takes tags as functional prior to mine the function-related features of services and Mashups, and a contextual attention mechanism that considers Mashup requirements as application scenario to help select the most appropriate services. We evaluate the proposed approach on a real-world dataset and the results show it has an improvement of 34 percent in F-measure over the basic LSTM model.
Novel image encryption algorithm based on improved logistic map

Chaotic mapping has been widely used in image encryption given the unpredictability, ergodicity, and sensitivity of parameters and initial values and the high correspondence with cryptography. The logistic map has the disadvantages of uneven distribution, low security, and small parameter space. In order to overcome these disadvantages, in this article, a new chaotic map based on a real-time variable logistic map with a randomly selected decimal is proposed. Furthermore, this chaotic mapping is applied to image encryption. Several simulation experiments show that the new encryption algorithm can obtain a safely encrypted image at a minimal time complexity.
Extractive single document summarization using multi-objective optimization: Exploring self-organized differential evolution, grey wolf optimizer and water cycle algorithm

Text summarization techniques become paramount in extracting relevant information from large databa-ses. Current paper attempts to build some extractive single document text summarization (ESDS) systems using multi-objective optimization (MOO) frameworks. Three techniques are proposed: (1) first is an integration of self-organizing map (SOM) and multi-objective differential evolution (MODE) (named as ESDS_SMODE) (2) second is based on multi-objective grey wolf optimizer (ESDS_MGWO) and (3) third is based on multi-objective water cycle algorithm (ESDS_MWCA). The sentences present in the document are first clustered utilizing the concept of multi-objective clustering. Two objective functions measuring compactness and separation of the sentence clusters in two different ways are optimized simultaneously using MOO framework. The proposed approach is able to automatically detect the number of sentence clusters present in a document and then representative sentences are selected from different clusters using some sentence scoring features to generate the summary. The experiments were conducted on two benchmark datasets, DUC2001, and DUC2002, and the obtained results are compared with various state-of-the-art techniques using ROUGE measures. Results illustrate the superiority of our approach in comparison to state-of-the-art techniques in terms of ROUGE−2 score for both datasets. Code of the developed approach ESDS_SMODE is available online at https://drive.google.com/open?id=1WagTeIDLgphttPrKHpnF_eO7QHWJxXxK.
Modified Genetic Algorithm approaches for classification of abnormal Magnetic Resonance Brain tumour images

Genetic Algorithm (GA) is one of the bio-inspired optimization techniques available for practical applications. The increasing necessity for bio-inspired optimization techniques has lead to the development of many innovative optimization techniques. In the backdrop, GA is completely forgotten and rarely used for practical applications. One of the significant reasons for the low preference of GA is the excessive "randomness" associated with this algorithm. The random nature of many processing steps in GA often leads to inaccurate results. The main focus of this research work is to enhance the usage of genetic algorithm for practical applications. Modified GA approaches are used in this work to overcome the drawback of the conventional approaches. In this research work, suitable modifications are made in the existing GA to minimize the random nature of conventional GA. Specifically, the focus of this work is to develop modified reproduction operators which forms the core part of this algorithm. Different binary operations are employed in this work to generate offspring in the process of crossover and mutation process. These binary operations are designed with a specific objective unlike the conventional binary operations in GA which are highly random in nature. The application of these approaches is explored in the context of medical image classification. Abnormal brain images from four different classes are used in this work. The proposed method has yielded 98% accuracy in comparison to other methods. Experimental results show promising results for the proposed approaches in terms of accuracy measures.
Approaches for Dialog Management in Conversational Agents

Dialog agents, like digital assistants and automated chat interfaces (e.g., chatbots), are becoming more and more popular as users adapt to conversing with their devices as they do with humans. In this paper, we present approaches and available tools for dialog management (DM), a component of dialog agents that handles dialog context and decides the next action for the agent to take. In this paper, we establish an overview of the field of DM, compare approaches and state-of-the-art tools in industry and research work on a set of dimensions, and identify directions for further research work.
X-Containers

"Cloud-native" container platforms, such as Kubernetes, have become an integral part of production cloud environments. One of the principles in designing cloud-native applications is called Single Concern Principle, which suggests that each container should handle a single responsibility well. In this paper, we propose X-Containers as a new security paradigm for isolating single-concerned cloud-native containers. Each container is run with a Library OS (LibOS) that supports multi-processing for concurrency and compatibility. A minimal exokernel ensures strong isolation with small kernel attack surface. We show an implementation of the X-Containers architecture that leverages Xen paravirtualization (PV) to turn Linux kernel into a LibOS. Doing so results in a highly efficient LibOS platform that does not require hardware-assisted virtualization, improves inter-container isolation, and supports binary compatibility and multi-processing. By eliminating some security barriers such as seccomp and Meltdown patch, X-Containers have up to 27X higher raw system call throughput compared to Docker containers, while also achieving competitive or superior performance on various benchmarks compared to recent container platforms such as Google's gVisor and Intel's Clear Containers.
Automatic Multiple Choice Question Generation From Text: A Survey

Automatic multiple choice question (MCQ) generation from a text is a popular research area. MCQs are widely accepted for large-scale assessment in various domains and applications. However, manual generation of MCQs is expensive and time-consuming. Therefore, researchers have been attracted toward automatic MCQ generation since the late 90's. Since then, many systems have been developed for MCQ generation. We perform a systematic review of those systems. This paper presents our findings on the review. We outline a generic workflow for an automatic MCQ generation system. The workflow consists of six phases. For each of these phases, we find and discuss the list of techniques adopted in the literature. We also study the evaluation techniques for assessing the quality of the system generated MCQs. Finally, we identify the areas where the current research focus should be directed toward enriching the literature.