Reviewerly/exHarmony huggingface dataset:
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search

One-shot neural architecture search (NAS) has played a crucial role in making NAS methods computationally feasible in practice. Nevertheless, there is still a lack of understanding on how these weight-sharing algorithms exactly work due to the many factors controlling the dynamics of the process. In order to allow a scientific study of these components, we introduce a general framework for one-shot NAS that can be instantiated to many recently-introduced variants and introduce a general benchmarking framework that draws on the recent large-scale tabular benchmark NAS-Bench-101 for cheap anytime evaluations of one-shot NAS methods. To showcase the framework, we compare several state-of-the-art one-shot NAS methods, examine how sensitive they are to their hyperparameters and how they can be improved by tuning their hyperparameters, and compare their performance to that of blackbox optimizers for NAS-Bench-101.
A 2020 perspective on “Digital blockchain networks appear to be following Metcalfe’s Law”

The value of several blockchain assets was shown to possibly be modeled based on Metcalfe’s law as well as a proposed network effects law in the original paper published in 2017. The models showed the growth in the value of the assets to be correlated to the growth in the number of daily unique addresses used on the network; data that is easily extracted from the blockchain. An update to that study is presented, extending the data from May 2017 to current, and showing the sustained correlation between current data and the models. A discussion of potential improvements to that model and its approach from a 2020 perspective is presented.
A Cross-Disciplinary Review of Blockchain Research Trends and Methodologies: Topic Modeling Approach

Given the increasing interest in blockchain technology, we present a large-scale cross-disciplinary literature analysis of research on the blockchain using topic modelling with the goal of identifying the major research trends, research methodologies, and fruitful areas for further research. In particular, the analysis focuses on abstracting out research trends from relevant terms and topics related to the research disciplines of Business, Computer Science, Economics, Social Sciences, Engineering, Healthcare, and Law. A total of 2,125 articles published between 2008 to up until early 2019 in academic journals and conferences were analyzed. Results of our analysis reveal that research is bipartite between practical and research domains, with academic research on blockchain not clearly aligning with organizational and social benefits. Also, we found – 1) few inter-disciplinary publications, and 2) a small number of studies that use surveys, experiments, and case studies as their research method. Our findings also reveal that research on Blockchain in the social sciences and law is still in the embryonic stage, thus making it essential to develop more direct research efforts for Blockchain to thrive in all research disciplines.
A Survey on Tracking Techniques in Augmented Reality based Application

Augmented Reality (AR) is a process where the computerized information is “augment” into the real or the physical world. This helps us in better understanding of the process and has numerous applications. The scope of AR is practically unlimited in today's world and if implemented, would help in solving many complex problems in an easy manner. This paper explains the applications of AR in multiple areas. The paper lays stress on six such major areas where the field of AR is flourished. The important milestones in the history of AR are discussed in this paper. Tracking is one of the thrust areas in the field of AR. A thorough survey is conducted on different types of tracking techniques that are used for development of AR applications. The paper is concluded in the form of comparative analysis between different types of sensor based tracking techniques and vision based tracking techniques. Hybridization of different types of tracking techniques is one of the best solutions for an accurate and robust tracking to meet the stringent requirements of AR applications.
Edge Detection Based on Fuzzy Logic and Hybrid Types of Shannon Entropy

Edge is basically the symbol and reflection of partial image discreteness. It is one of the most commonly used operations in image processing and pattern recognition, it contains a wealth of internal information leading to strong interpretation of image. Resisting against noise, illumination and extracting appropriate features from an image is a great challenge in many computer vision applications. Indeed this topic participates to reduce the handled information and focuses on those related to existing objects. Efficient and accurate edge detection will lead to increase in the performance of many computer vision applications, including image segmentation, object-based image coding and image retrieval. Contour detection contributes to locate pixel sets which correspond to sudden intensities variation, these unstable properties of the given image commonly suggest to important events on going in the scene. In this paper, we present in the first time a novel and robust method for edge detection based on joint and conditional entropy when we highlight a Shannon theory, the second part of this paper is dedicated to decision making of edge pixels membership by intelligent method based on fuzzy logic tool.
Particle Swarm Optimization Algorithm Based on Factor Selection Strategy

The classical particle swarm optimization algorithm will cause premature stagnation due to the limitation of particle diversity, and thus fall into local optimum. In order to overcome the above shortcomings, this paper proposes a particle swarm optimization algorithm based on particle swarm number selection strategy based on the idea of particle swarm. In the different stages of the particle swarm search process, different parameter selection strategies are adopted to improve the global search ability. The simulation results show that the method is effective and superior in solving the optimization problem of complex problems.
Decentralised Internet of Things

The growing number of IoT devices brings challenges to the existing centralised computing system. The existing security protocols are unable to protect the security and privacy of the user data. The current IoT system rely on centralised model. The decentralised IoT system would not only reduce the infrastructure cost but provide standardised peer-to-peer communication model for the massive transactions. However, peer-to-peer communication model has a big challenge of security. The blockchain technology ensures transparent interactions between different parties in a more secure and trusted way using distributed ledger and proof-of-work (POW) consensus algorithm. Blockchain enables trustless, peer-to-peer communication and has already proven its worth in the world of financial services. The idea of blockchain can be implanted to IoT system to deal with the issue of scale, trustworthy and decentralisation, thereby allowing billions of devices to share the same network without the need for additional resources. However, the limited processing power, storage size and energy consumption of IoT device is a major point of concern for blockchain cryptographic functions. Moreover, efficiency, reliability, interoperability among blockchain still need to be addressed. This chapter presents basic concepts of blockchain and investigation about the feasibility of the blockchain in Internet of Things settings.
PPLS: a privacy-preserving location-sharing scheme in mobile online social networks

The recent proliferation of mobile devices has given rise to mobile online social networks (mOSNs), an emerging network paradigm that uses social networks as its main design element. As one of the most critical components in mOSNs, location sharing plays an important role in helping users share information and strengthen their social bonds, which however may compromise users' privacy, including location information and social relationship details. To address these challenges, some solutions have been proposed. However, none of them considers the privacy of inter-user threshold distance, which effectively can be used to identify users, their friends, and location information, by malicious or undesired elements of the system. To overcome this limitation, we propose a secure distance comparison protocol. Furthermore, we present a privacy-preserving location-sharing scheme (PPLS), which allows users to build more complex access control policies. The safety of our scheme is validated by the security analysis and the experimental results demonstrate the efficiency of PPLS scheme.
Information Extraction from Cancer Pathology Reports with Graph Convolution Networks for Natural Language Texts

Graph-of-words is a flexible and efficient text representation which addresses well-known challenges, such as word ordering and variation of expressions, to natural language processing. In this paper, we consider the latest graph-based convolutional neural network technique, the Text GraphConvolutional Network (Text GCN), in the context of performingclassification tasks on free-form natural language texts. To do this, we designed a study of multi-task information extraction from medical text documents. We implemented multi-task learning in the Text GCN, performed hyperparameter optimization, and measured the clinical task performances. We evaluated micro and macro-F1 scores of four information extraction tasks,including subsite, laterality, behavior, and histological grades from cancer pathology reports. The scores for the Text GCN significantly outperformed our previous studies with convolutional neural networks, suggesting that the Text GCN model is superior to traditional models in task performance.
Is Static Analysis Able to Identify Unnecessary Source Code?

Grown software systems often contain code that is not necessary anymore. Such unnecessary code wastes resources during development and maintenance, for example, when preparing code for migration or certification. Running a profiler may reveal code that is not used in production, but it is often time-consuming to obtain representative data in this way. We investigate to what extent a static analysis approach, which is based on code stability and code centrality, is able to identify unnecessary code and whether its recommendations are relevant in practice. To study the feasibility and usefulness of our approach, we conducted a study involving 14 open-source and closed-source software systems. As there is no perfect oracle for unnecessary code, we compared recommendations for unnecessary code with historical cleanups, runtime usage data, and feedback from 25 developers of five software projects. Our study shows that recommendations generated from stability and centrality information point to unnecessary code that cannot be identified by dead code detectors. Developers confirmed that 34% of recommendations were indeed unnecessary and deleted 20% of the recommendations shortly after our interviews. Overall, our results suggest that static analysis can provide quick feedback on unnecessary code and is useful in practice.
A Survey on Image Style Transfer Approaches Using Deep Learning

Abstract Image style transfer has been an important branch of computer vision and image processing. Inspired by the development of deep learning, applications of Convolutional Neural Networks (CNNs) in recomposing content and style of two separated images were proven to be effective by the recent works of image style transfer. This paper provides a survey of image style transfer, which focuses on methods which using deep learning. In addition, this paper also provides information of methods without deep learning, and seeks their points of innovation. In this paper, all references are published ranging from 2001 to 2017, which presents an overview of progress in this realm over the past two decades.
Exploiting Multi-Task Learning to Achieve Effective Transfer Deep Reinforcement Learning in Elastic Optical Networks

We propose a multi-task-learning-aided knowledge transferring approach for effective and scalable deep reinforcement learning in EONs. Case studies with RMSA show that this approach can achieve ∼ 4 × learning time reduction and ∼ 17 . 7% lower blocking probability.
A Vector-Based Constrained Obstacle Avoidance Scheme for Wheeled Mobile Redundant Robot Manipulator

Obstacle avoidance is an important issue when a wheeled mobile redundant robot manipulator (WMRRM) completes given tasks in complex environment. In this article, a novel vector-based constrained obstacle avoidance (VOA) scheme is designed and analyzed for motion planning of the WMRRM. Specifically, the VOA scheme is first described as a quadratic programming (QP) problem subject to an equality constraint, an inequality constraint, and a bound constraint. Compared with the traditional obstacle avoidance scheme, the proposed VOA scheme can not only achieve subtasks, such as obstacle avoidance and physical limit avoidance when the robot completes the main end-effector task, but also greatly expands the range of feasible spaces. In addition, the VOA scheme is transformed into piecewise linear projection equations (PLPEs) and solved by a linear-variational-inequality-based primal-dual neural network (LVI-PDNN), which can efficiently obtain the optimal solutions to the VOA scheme. Computer simulations demonstrate the reliability, accuracy, and effectiveness of the proposed VOA scheme.
Computing the optimal ate pairing over elliptic curves with embedding degrees 54 and 48 at the 256-bit security level

Due to recent advances in the computation of finite fields discrete logarithms, the Barreto-Lynn-Scott family of elliptic curves of embedding degree 48 became suitable for instantiating pairing-based cryptography at the 256-bit security level. Observing the uncertainty around determining the constants that govern the best approach for computing discrete logarithms, Scott and Guillevic consider pairing-friendly elliptic curves of embedding degree higher than 50, and discovered a new family of elliptic curves with embedding degree 54. This work aims at investigating the theoretical and practical cost of both the Miller algorithm and the final exponentiation in the computation of the optimal ate pairing on the two aforementioned curves. Both our theoretical results, based on the operation counts of base-field operations, and our experimental observations collected from a real implementation, confirm that BLS48 curves remain the faster curve in the computation of the optimal ate pairing at the 256-bit security level.
Using a Chatbot, Replika, to Practice Writing Through Conversations in L2 English

This chapter explores university students' views of Replika, an English chatbot. Students in a department of health administration at a state university in Turkey used Replika to complete different tasks over 7 weeks. At the end of the study, semi-structured interviews were conducted with 10 randomly selected students. They liked using Replika and found the software useful. The participants underscored the importance of receiving an immediate response to what they wrote on their mobile devices and added that they edited their sentences when Replika could not understand the message they were trying to convey.
Topic Segmentation for Dialogue Stream

Topic segmentation, which aims to divide a document into topic blocks, is a fundamental task in natural language processing. Most of the previous researches focus on written text rather than dialogue text. However, dialogue text has its unique characteristic and is more challenging in topic segmentation. The existing neural models for topic segmentation are usually built on RNN or CNN, which are competent in written text but has a poor performance in dialogue text. We argue that a better segmentation result for dialogue text requires a better semantic representation of sentences. In this paper, we formulate topic segmentation as a sequence labeling task and propose a model based on BERT and TCN (Temporal Convolutional Network) to accomplish the task. We also present three datasets, including two dialogue datasets and a news dataset, to evaluate the model's performance. Compared to the previous best model, our model shows an absolute performance improvement of 8% - 17% in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> scores. Moreover, we explore the impact of importing speakers on dialogue text segmentation, the experiment result shows that the additional speaker information could effectively improve the segmentation performance.
Efficient FPGA implementation of corrected reversible contrast mapping algorithm for video watermarking

This paper analyses and rectifies the shortcomings of reversible contrast mapping (RCM) algorithm for invisible watermarking. The proposed corrected RCM algorithm is tested by taking a gray scaled video input. The quality of services and structural similarity index matrix (SSIM) of each frame of the input video are tested in software environment. The video is obtained by OV7670 camera through Zed-board in fully field programmable gate array (FPGA) based hardware environment. FPGA devices based corrected high level synthesis of the proposed algorithm is presented. The suggested system engages pipeline structure and practices parallelism to achieve high performance. The quality of services and SSIM are also tested using FPGA devices and the comparative results with software implementations are explained. To process thirty (640 × 480) image blocks with 150 MHz clock we obtain a latency of 876.626 ns with throughput 62.328 Mbps. The critical path for single cycle is 5.992 ns. The number of resources essential is similar for watermark decoding with an improved schedule. The results acquired after implementation on Xilinx Virtex 7-XC7V2000T and programmable system-on-chip (Zynq - XC7Z030) FPGA devices confirm the practicability of real-time use with low cost and great speed.
The dual angle solar harvest (DASH) method: An alternative method for organizing large solar panel arrays that optimizes incident solar energy in conjunction with land use

Plummeting costs of solar panels along with rising costs of land makes it imperative to consider both the per-panel and unit-area incident solar energy. An alternative approach of organizing large solar panel arrays that considers this co-optimization problem is suggested and a new dual-angle technique is introduced, called the dual-angle solar harvest (DASH) method, in which a solar array is composed of two tilt angles. This paper evaluates the DASH method at two climatically and geographically different locations: Akron, OH and Barstow, CA. Use of the DASH method can enable potential better handling of complex radiation fields in partly cloudy climates, allow for preferential weighting of irradiation during different seasons, and may be a tool to increase array-wide incident irradiation at a pre-existing array in a cost-effective manner. This latter example is tested nationwide, in which one angle is constrained at the single optimum tilt angle. Results show the lowest percentage of panel-rows that require an adjustment to a different tilt angle to achieve the desired gain in array-wide incident energy. The more cost-effective solutions tend to be located in cloudier climates and at higher latitudes.
Is there a correlation between code comments and issues?

Comments in a software code base are one of the key artifacts that help developers in understanding the code with respect to development and maintenance. Comments provide us with the information that is used as a software metric to assess the code quality and which further can be applied to demonstrate its impact on the issues in the code. In this paper, we set out to understand the correlation between code comments and issues in Github. We conduct an empirical study on 625 repositories hosted on GitHub with Python as their primary language. We manually classify comments from a randomly selected sample of python repositories and then train and evaluate classifiers to automatically label comments as Relevant or Auxiliary. We extract the metadata of issues in each repository present in our dataset and perform various experiments to understand the correlation between code comments and issues. From our dataset of python repositories, we then plot a graph between the average time taken to resolve an issue and percentage of relevant comments in a repository to find if there is any relation or a pattern by which the latter affects the former. Our statistical approach of finding out the correlation between code comments and issues gives us the correlation factor by which code comments are related to issues. We conclude from our study that comments are indeed important and play an important role in solving issues of the project. We also found that increasing the percentage of relevant comments along with the source code can help in the reduction of the average number of days before an issue is resolved.
Using a small amount of text-independent speech data for a BiLSTM large-scale speaker identification approach

Communication between people and machines has been extended in the last two decades. Corresponding techniques have been founded to cover the need of voice understanding, including speech and speaker recognition on a large-scale. In this paper, the authors propose a simplified deep-learning approach to accomplish the large-scale speaker identification task using as little training data as possible. Fisher speech corpus has been explored to select the recordings of unique speakers having sufficient data. The authors are using the MFCC method to represent the feature vectors of a large set of more than 4 k speakers with about 343 h of speech signals. The solution includes omitting the pre-processing and considering longer segments of the voice signals. Various portions of training datasets have been tested, as well as dedicating larger percentages of the used data for testing. Bidirectional LSTM neural networks provided up to 76.9% accuracy rate for individual voice segments, and 99.5% when considering the segments of each speaker as a bundle. Doubling the amount of the training data yielded a perfect accuracy rate of 100%.
Improving Efficiency in Large-Scale Decentralized Distributed Training

Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchronous Parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to perform well for large-scale deep learning tasks. One drawback of (A)D-PSGD is that the spectral gap of the mixing matrix decreases when the number of learners in the system increases, which hampers convergence. In this paper, we investigate techniques to accelerate (A)D-PSGD based training by improving the spectral gap while minimizing the communication cost. We demonstrate the effectiveness of our proposed techniques by running experiments on the 2000-hour Switchboard speech recognition task and the ImageNet computer vision task. On an IBM P9 supercomputer, our system is able to train an LSTM acoustic model in 2.28 hours with 7.5% WER on the Hub5-2000 Switchboard (SWB) test set and 13.3% WER on the CallHome (CH) test set using 64 V100 GPUs and in 1.98 hours with 7.7% WER on SWB and 13.3% WER on CH using 128 V100 GPUs, the fastest training time reported to date.