Reviewerly/exHarmony huggingface dataset:
Timing and Resource-Aware Mapping of Quantum Circuits to Superconducting Processors

Quantum algorithms need to be compiled to respect the constraints imposed by quantum processors, which is known as the mapping problem. The mapping procedure will result in an increase of the number of gates and of the circuit latency, decreasing the algorithm's success rate. It is crucial to minimize mapping overhead, especially for Noisy Intermediate-Scale Quantum (NISQ) processors that have relatively short qubit coherence times and high gate error rates. Most of prior mapping algorithms have only considered constraints such as the primitive gate set and qubit connectivity, but the actual gate duration and the restrictions imposed by the use of shared classical control electronics have not been taken into account. In this paper, we present a timing and resource-aware mapper called Qmap to make quantum circuits executable on a scalable superconducting processor named Surface-17 with the objective of achieving the shortest circuit latency. In particular, we propose an approach to formulate the classical control restrictions as resource constraints in a conventional list scheduler with polynomial complexity. Furthermore, we implement a routing heuristic to cope with the connectivity limitation. This router finds a set of movement operations that minimally extends circuit latency. To analyze the mapping overhead and evaluate the performance of different mappers, we map 56 quantum benchmarks onto Surface-17. Compared to a prior mapping strategy that minimizes the number of operations, Qmap can reduce the latency overhead up to 47.3% and operation overhead up to 28.6%, respectively.
Best Practices of Convolutional Neural Networks for Question Classification

Question Classification (QC) is of primary importance in question answering systems, since it enables extraction of the correct answer type. State-of-the-art solutions for short text classification obtained remarkable results by Convolutional Neural Networks (CNNs). However, implementing such models requires choices, usually based on subjective experience, or on rare works comparing different settings for general text classification, while peculiar solutions should be individuated for QC task, depending on language and on dataset size. Therefore, this work aims at suggesting best practices for QC using CNNs. Different datasets were employed: (i) A multilingual set of labelled questions to evaluate the dependence of optimal settings on language; (ii) a large, widely used dataset for validation and comparison. Numerous experiments were executed, to perform a multivariate analysis, for evaluating statistical significance and influence on QC performance of all the factors (regarding text representation, architectural characteristics, and learning hyperparameters) and some of their interactions, and for finding the most appropriate strategies for QC. Results show the influence of CNN settings on performance. Optimal settings were found depending on language. Tests on different data validated the optimization performed, and confirmed the transferability of the best settings. Comparisons to configurations suggested by previous works highlight the best classification accuracy by those optimized here. These findings can suggest the best choices to configure a CNN for QC.
Energy Efficient Algorithms based on VM Consolidation for Cloud Computing: Comparisons and Evaluations

Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility.With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime.Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy.However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment.Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques.One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption.Currently, various virtual machine consolidationbased energy efficient algorithms have been proposed to reduce the energy of cloud computing environment.However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings.This makes users hard to select the appropriate algorithm for their objectives.To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics.In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit.The experimental results show the performance comparison of these algorithms with comprehensive results.Finally, detailed discussions of these algorithms are provided.
Performance Evaluation of an Independent Time Optimized Infrastructure for Big Data Analytics that Maintains Symmetry

Traditional data analytics tools are designed to deal with the asymmetrical type of data i.e., structured, semi-structured, and unstructured. The diverse behavior of data produced by different sources requires the selection of suitable tools. The restriction of recourses to deal with a huge volume of data is a challenge for these tools, which affects the performances of the tool’s execution time. Therefore, in the present paper, we proposed a time optimization model, shares common HDFS (Hadoop Distributed File System) between three Name-node (Master Node), three Data-node, and one Client-node. These nodes work under the DeMilitarized zone (DMZ) to maintain symmetry. Machine learning jobs are explored from an independent platform to realize this model. In the first node (Name-node 1), Mahout is installed with all machine learning libraries through the maven repositories. The second node (Name-node 2), R connected to Hadoop, is running through the shiny-server. Splunk is configured in the third node (Name-node 3) and is used to analyze the logs. Experiments are performed between the proposed and legacy model to evaluate the response time, execution time, and throughput. K-means clustering, Navies Bayes, and recommender algorithms are run on three different data sets, i.e., movie rating, newsgroup, and Spam SMS data set, representing structured, semi-structured, and unstructured data, respectively. The selection of tools defines data independence, e.g., Newsgroup data set to run on Mahout as others cannot be compatible with this data. It is evident from the outcome of the data that the performance of the proposed model establishes the hypothesis that our model overcomes the limitation of the resources of the legacy model. In addition, the proposed model can process any kind of algorithm on different sets of data, which resides in its native formats.
Study of Security Mechanisms to Create a Secure Cloud in a Virtual Environment with the Support of Cloud Service Providers

In this paper, we are focussing on creating a secure cloud environment with the help of the cloud service provides. Nowadays, the demand for the cloud increases and all the business are transferred to the cloud environment. When a greater number of people get involved, security matters a lot. This paper emphases on secured cloud environment by creating a trusted relationship with the cloud service provider and also by comparing the current security mechanisms applied in the real-world businesses. This paper helps the readers to understand the need of security mechanisms in the cloud environment and the need for the SLAs with the trusted Cloud Service Providers.
Assuring the Safety of Machine Learning for Pedestrian Detection at Crossings

Machine Learnt Models (MLMs) are now commonly used in self-driving cars, particularly for tasks such as object detection and classification within the perception pipeline. The failure of such models to perform as intended could lead to hazardous events such as failing to stop for a pedestrian at a crossing. It is therefore crucial that the safety of the MLM can be proactively assured and should be driven by explicit and concrete safety requirements. In our previous work, we defined a process that integrates the development and assurance activities for MLMs within safety-related systems. This is used to incrementally generate the safety argument and evidence. In this paper, we apply the approach to pedestrian detection at crossings and provide an evaluation using the publicly available JAAD data set. In particular, we focus on the elicitation and analysis of ML safety requirements and how such requirements should drive the assurance activities within the data management and model learning phases. We explain the benefits of the approach and identify outstanding challenges in the context of self-driving cars.
Any-to-Many Voice Conversion With Location-Relative Sequence-to-Sequence Modeling

This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq), non-parallel voice conversion approach, which utilizes text supervision during training. In this approach, we combine a bottle-neck feature extractor (BNE) with a seq2seq synthesis module. During the training stage, an encoder-decoder-based hybrid connectionist-temporal-classification-attention (CTC-attention) phoneme recognizer is trained, whose encoder has a bottle-neck layer. A BNE is obtained from the phoneme recognizer and is utilized to extract speaker-independent, dense and rich spoken content representations from spectral features. Then a multi-speaker location-relative attention based seq2seq synthesis model is trained to reconstruct spectral features from the bottle-neck features, conditioning on speaker representations for speaker identity control in the generated speech. To mitigate the difficulties of using seq2seq models to align long sequences, we down-sample the input spectral feature along the temporal dimension and equip the synthesis model with a discretized mixture of logistic (MoL) attention mechanism. Since the phoneme recognizer is trained with large speech recognition data corpus, the proposed approach can conduct any-to-many voice conversion. Objective and subjective evaluations show that the proposed any-to-many approach has superior voice conversion performance in terms of both naturalness and speaker similarity. Ablation studies are conducted to confirm the effectiveness of feature selection and model design strategies in the proposed approach. The proposed VC approach can readily be extended to support any-to-any VC (also known as one/few-shot VC), and achieve high performance according to objective and subjective evaluations.
Enhanced Visual Attention-Guided Deep Neural Networks for Image Classification

A fully connected layer is essential for a CNN, i.e., convolutional neural network, which has been shown to be successful in classifying images in several related applications.A CNN begins with convolution and pooling operations for decomposing an input image into features.The result of this process is then fed into a fully connected neural network, driving the final classification decision for the input image.However, it has been found that the learned feature maps in a CNN are sometimes not good enough for being fed into the fully connected layers to get good classification results.In this article, a visual attention learning module is proposed to enhance the classification capability of the fully connected layers in a CNN.By learning better feature maps to emphasize salient regions and weaken meaningless regions, better classification performance can be obtained with integrating the proposed module into the fully connected layers.The proposed visual attention learning module can be imposed on any existed CNN-based image classification models to achieve incremental improvements with negligible overhead.Based on our experiments, the proposed method achieves the top-1 accuracies of 95.32%, 92.73%, and 66.50% on average, respectively, obtained on our collected Underwater Fish dataset, the public Animals-10 dataset, and the public Stanford Cars dataset.
Assessing Blockchain Technology application for freight booking business: a case study from Technology Acceptance Model perspective

Purpose The purpose of this study is to develop a framework for a freight consolidation company to adopt blockchain for the shipping community. Our research critically examine the challenges faced by a global shipping company that offer freight consolidation businesses and explore the use of Blockchain technology to enhance the competitiveness and sustainability of freight booking operations. Design/methodology/approach This paper is a case study, ECU Worldwide, with focus on transforming their operations using blockchain technology for the freight booking industry. As the case is explorative in nature, the research aim to unearth the complex blockchain adoption phenomenon in the industry as the technology is very nascent at present. The research is primarily grounded on Technology Acceptance Model (TAM) theory. Findings The research finds that blockchain technology supports solving many issues and inefficiencies of global shipping operations but there are some barriers that they need to overcome. The research provides a framework and recommendations for global company to consider when considering Blockchain technology for implementation. Our research finding shows that smart contracts can be set up at critical points along with the shipment route namely the storage, customs, carrier, transporters and consignee stage to ensure greater security and transparency. Research limitations/implications The research provides recommendations to key stakeholders involved in freight forwarding segment of logistics industry while adopting blockchain technology. Apart from the methodological limitation of the research, the research is limited to Singapore in terms of geographical coverage. Practical implications The drivers and barriers identified in the study can give practitioners insight of using blockchain for the industry. The proposed framework can assist companies in the shipping industry to prepare themselves to adopt blockchain for the community. Originality/value This case study is the first of its kind to examine the use of blockchain to explore the adoption in logistics Industry in Singapore and perhaps worldwide.
Traditional Method Inspired Deep Neural Network For Edge Detection

Recently, Deep-Neural-Network (DNN) based edge prediction is progressing fast. Although the DNN based schemes outperform the traditional edge detectors, they have much higher computational complexity. It could be that the DNN based edge detectors often adopt the neural net structures designed for high-level computer vision tasks, such as image segmentation and object recognition. Edge detection is a rather local and simple job, the over-complicated architecture and massive parameters may be unnecessary. Therefore, we propose a traditional method inspired framework to produce good edges with minimal complexity. We simplify the network architecture to include Feature Extractor, Enrichment, and Summarizer, which roughly correspond to gradient, low pass filter, and pixel connection in the traditional edge detection schemes. The proposed structure can effectively reduce the complexity and retain the edge prediction quality. Our TIN2 (Traditional Inspired Network) model has an accuracy higher than the recent BDCN2 (Bi-Directional Cascade Network) but with a smaller model.
SelfPose: 3D Egocentric Pose Estimation From a Headset Mounted Camera

We present a solution to egocentric 3D body pose estimation from monocular images captured from downward looking fish-eye cameras installed on the rim of a head mounted VR device. This unusual viewpoint leads to images with unique visual appearance, with severe self-occlusions and perspective distortions that result in drastic differences in resolution between lower and upper body. We propose an encoder-decoder architecture with a novel multi-branch decoder designed to account for the varying uncertainty in 2D predictions. The quantitative evaluation, on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric approaches. To tackle the lack of labelled data we also introduced a large photo-realistic synthetic dataset. xR-EgoPose offers high quality renderings of people with diverse skintones, body shapes and clothing, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of theart results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint.
Adversarial Video Moment Retrieval by Jointly Modeling Ranking and Localization

Retrieving video moments from an untrimmed video given a natural language as the query is a challenging task in both academia and industry. Although much effort has been made to address this issue, traditional video moment ranking methods are unable to generate reasonable video moment candidates and video moment localization approaches are not applicable to large-scale retrieval scenario. How to combine ranking and localization into a unified framework to overcome their drawbacks and reinforce each other is rarely considered. Toward this end, we contribute a novel solution to thoroughly investigate the video moment retrieval issue under the adversarial learning paradigm. The key of our solution is to formulate the video moment retrieval task as an adversarial learning problem with two tightly connected components. Specifically, a reinforcement learning is employed as a generator to produce a set of possible video moments. Meanwhile, a pairwise ranking model is utilized as a discriminator to rank the generated video moments and the ground truth. Finally, the generator and the discriminator are mutually reinforced in the adversarial learning framework, which is able to jointly optimize the performance of both video moment ranking and video moment localization. Extensive experiments on two well-known datasets have well verified the effectiveness and rationality of our proposed solution.
A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection

Automatic reading of pointer meters is of great significance for efficient measurement of industrial meters. However, existing algorithms are defective in the accuracy and robustness to illumination shooting angle when detecting various pointer meters. Hence, a novel algorithm for adaptive detection of different pointer meters was presented. Above all, deep learning was introduced to detect and recognize scale value text in the meter dial. Then, the image was rectified and meter center was determined based on text coordinate. Next, the circular arc scale region was transformed into a linear scale region by polar transform, and the horizontal positions of pointer and scale line were obtained based on secondary search in the expanded graph. Finally, the distance method was used to read the scale region where the pointer is located. Test results showed that the algorithm proposed in this paper has higher accuracy and robustness in detecting different types of meters.
Color image encryption based on chaotic compressed sensing and two-dimensional fractional Fourier transform

Combining the advantages of structured random measurement matrix and chaotic structure, this paper introduces a color image encryption algorithm based on a structural chaotic measurement matrix and random phase mask. The Chebyshev chaotic sequence is used in the algorithm to generate the flip permutation matrix, the sampling subset and the chaotic cyclic matrix for constructing the structure perceptual matrix and the random phase mask. The original image is compressed and encrypted simultaneously by compressed sensing, and re-encrypted by two-dimensional fractional Fourier transform. Simulation experiments show the effectiveness and reliability of the algorithm.
Towards a hybrid project management framework: A systematic literature review on traditional, agile and hybrid techniques

It is the state of the practice in these turbulent times to combine different approaches and project management methods into hybrid and adaptive methods according to the specific needs of the project and the project team. The objective of the present work is to investigate this practice conducting a systematic literature review on project management methods, hybrid agile methods, method tailoring and innovative processes to tailor methodologies. We applied a systematic analysis of the existent research published during period 2000 – 2020 following similar approaches of several scholars. 1.121 articles published from 2000 until 2020 were collected after exhaustive literature search. However, 98 of them were selected for further investigation according to the criteria selection. Results provide key insights and background on agile project management frameworks as well as challenges and best practices implementing an inhouse hybrid agile project management framework. We anticipate the present work to constitute a useful source of information to PM scientists and practitioners. Results identify new dimensions emerging of the synthesis of project management aspects and provide key insights on traditional and agile project management frameworks as well as challenges and best practices implementing an inhouse tailored hybrid agile project management framework. This work contributes to the body of knowledge about the organization mainly of agile and hybrid approaches. We analyzed the found descriptions thoroughly regarding their characteristics and their organization.
On the search for environmental sustainability in Africa: the role of governance

Africa remains the most affected by environmental degradation, thereby exacerbating the negative effect of climate change in the region. Little empirical credence has been leaned to the institution-environmental sustainability relationship in Africa. This omission in the literature of environmental sustainability is abysmal, considering the role of institutions and government in ecological preservation. To inform policy and research on the subject matter, we estimated a unbalanced panel data of the indices of good governance and strong institutions to explain transformation to environmental sustainability using the dynamic system generalised method of moment estimator from 1996 through 2017. Findings suggested a positive relationship between the rule of law and regulatory quality and transformation to environmental sustainability. An inverse relationship between government effectiveness and environmental sustainability was established. We recommended concerted effort at an institutional level such that policy and punishment for violation of greenhouse strategies will be optimum.