Reviewerly/exHarmony huggingface dataset:
Generating concept based API element comparison using a knowledge graph

Developers are concerned with the comparison of similar APIs in terms of their commonalities and (often subtle) differences. Our empirical study of Stack Overflow questions and API documentation confirms that API comparison questions are common and can often be answered by knowledge contained in API reference documentation. Our study also identifies eight types of API statements that are useful for API comparison. Based on these findings, we propose a knowledge graph based approach APIComp that automatically extracts API knowledge from API reference documentation to support the comparison of a pair of API classes or methods from different aspects. Our approach includes an offline phase for constructing an API knowledge graph, and an online phase for generating an API comparison result for a given pair of API elements. Our evaluation shows that the quality of different kinds of extracted knowledge in the API knowledge graph is generally high. Furthermore, the comparison results generated by APIComp are significantly better than those generated by a baseline approach based on heuristic rules and text similarity, and our generated API comparison results are useful for helping developers in API selection tasks.
NeuroDiff

As neural networks make their way into safety-critical systems, where misbehavior can lead to catastrophes, there is a growing interest in certifying the equivalence of two structurally similar neural networks - a problem known as differential verification. For example, compression techniques are often used in practice for deploying trained neural networks on computationally- and energy-constrained devices, which raises the question of how faithfully the compressed network mimics the original network. Unfortunately, existing methods either focus on verifying a single network or rely on loose approximations to prove the equivalence of two networks. Due to overly conservative approximation, differential verification lacks scalability in terms of both accuracy and computational cost. To overcome these problems, we propose NeuroDiff, a symbolic and fine-grained approximation technique that drastically increases the accuracy of differential verification on feed-forward ReLU networks while achieving many orders-of-magnitude speedup. NeuroDiff has two key contributions. The first one is new convex approximations that more accurately bound the difference of two networks under all possible inputs. The second one is judicious use of symbolic variables to represent neurons whose difference bounds have accumulated significant error. We find that these two techniques are complementary, i.e., when combined, the benefit is greater than the sum of their individual benefits. We have evaluated NeuroDiff on a variety of differential verification tasks. Our results show that NeuroDiff is up to 1000X faster and 5X more accurate than the state-of-the-art tool.
Did the introduction of Bitcoin futures crash the Bitcoin market at the end of 2017?

At the end of 2017, the Bitcoin price dropped significantly by approximately 70% over the two months. Since the introduction of Bitcoin futures coincided with this market crash, it is said that the new financial instrument might have caused the market crash. The literature states that the futures enabled investors to easily take a short position and hypothesizes that the selling pressure from futures could have potentially crashed the Bitcoin market. To evaluate this assumption, we investigate the empirical relationship between futures trading and the Bitcoin price by using high-frequency data. We find that Bitcoin futures trading was not significantly related to the returns on Bitcoin futures and spot returns. Therefore, we conclude that Bitcoin futures did not lead to the crash of the Bitcoin market at the end of 2017.
CAUDHT: Decentralized Contact Tracing Using a DHT and Blind Signatures

Contact tracing is a promising approach to combat the COVID-19 pandemic.Various systems have been proposed to automatise the process.Many designs rely heavily on a centralised server or reveal significant amounts of private data to health authorities.We propose CAUDHT, a decentralized peer-to-peer system for contact tracing.The central health authority can focus on providing and operating tests for the disease while contact tracing is done by the system's users themselves.We use a distributed hash table to build a decentral messaging system for infected patients and their contacts.With blind signatures, we ensure that messages about infections are authentic and unchanged.A strong privacy focus enables data integrity, confidentiality, and privacy.
Virtual humans as co-workers: A novel methodology to study peer effects

We introduce a novel methodology to study peer effects. Using virtual reality technology, we create a naturalistic work setting in an immersive virtual environment where we embed a computer-generated virtual human as the co-worker of a human subject, both performing a sorting task at a conveyor belt. In our setup, subjects observe the virtual peer, while the virtual human is not observing them. In two treatments, human subjects observe either a low productive or a highly productive virtual peer. We find that human subjects rate their presence feeling of "being there" in the immersive virtual environment as natural. Subjects also recognize that virtual peers in our two treatments showed different productivities. We do not find a general treatment effect on productivity. However, we find that competitive subjects display higher performance when they are in the presence of a highly productive peer - compared to when they observe a low productive peer. We use tracking data to learn about the subjects' body movements. Analyzing hand and head data, we show that competitive subjects are more careful in the sorting task than non-competitive subjects. We also discuss some VR related methodological issues.
Illustrative Discussion of MC-Dropout in General Dataset: Uncertainty Estimation in Bitcoin

Abstract The past few years have witnessed the resurgence of uncertainty estimation generally in neural networks. Providing uncertainty quantification besides the predictive probability is desirable to reflect the degree of belief in the model’s decision about a given input. Recently, Monte-Carlo dropout (MC-dropout) method has been introduced as a probabilistic approach based Bayesian approximation which is computationally efficient than Bayesian neural networks. MC-dropout has revealed promising results on image datasets regarding uncertainty quantification. However, this method has been subjected to criticism regarding the behaviour of MC-dropout and what type of uncertainty it actually captures. For this purpose, we aim to discuss the behaviour of MC-dropout on classification tasks using synthetic and real data. We empirically explain different cases of MC-dropout that reflects the relative merits of this method. Our main finding is that MC-dropout captures datapoints lying on the decision boundary between the opposed classes using synthetic data. On the other hand, we apply MC-dropout method on dataset derived from Bitcoin known as Elliptic data to highlight the outperformance of model with MC-dropout over standard model. A conclusion and possible future directions are proposed.
Video Anomaly Detection using Pre-Trained Deep Convolutional Neural Nets and Context Mining

Anomaly detection is critically important for intelligent surveillance systems to detect in a timely manner any malicious activities. Many video anomaly detection approaches using deep learning methods focus on a single camera video stream with a fixed scenario. These deep learning methods use large-scale training data with large complexity. As a solution, in this paper, we show how to use pre-trained convolutional neural net models to perform feature extraction and context mining, and then use denoising autoencoder with relatively low model complexity to provide efficient and accurate surveillance anomaly detection, which can be useful for the resource-constrained devices such as edge devices of the Internet of Things (IoT). Our anomaly detection model makes decisions based on the high-level features derived from the selected embedded computer vision models such as object classification and object detection. Additionally, we derive contextual properties from the high-level features to further improve the performance of our video anomaly detection method. We use two UCSD datasets to demonstrate that our approach with relatively low model complexity can achieve comparable performance compared to the state-of-the-art approaches.
Blockchain-based scheme for the mobile number portability

Regular up-gradation in the standards of the wireless networks and to sustain in the market with the best services, are the key challenges to the services providers of the telecommunication industries. Traditional mobile number portability system (TMNPS) is based on the manual process, in which existing service provider and the new service provider have to undergo certain transfers once a porting request is generated by the user. Slow processing of request, lack of transparency among the entities involved, and delay during the verification are the major challenges in the TMNPS. Moreover, a third-party is required to ensure the security of the stored and regulated data. Motivated from these facts, in this paper, we propose a blockchain-based mobile number portability scheme, which uses an Ethereum blockchain technology and call routing mechanism to handle the request of the user. The secured royalty contract transactions and secure call routing mechanism are the potential part of the proposed scheme. We then evaluated the performance of the proposed scheme and functionalities of the proposed smart contracts (SCs) using experimental setup with respect to various parameters. Results show that the transition time taken by the proposed scheme is only 4 seconds for 200 blocks and TMNPS took 6 seconds to execute the 200 blocks.
Entanglement is Necessary for Optimal Quantum Property Testing

There has been a surge of progress in recent years in developing algorithms for testing and learning quantum states that achieve optimal copy complexity [1]–[6]. Unfortunately, they require the use of entangled measurements across many copies of the underlying state and thus remain outside the realm of what is currently experimentally feasible. A natural question is whether one can match the copy complexity of such algorithms using only independent-but possibly adaptively chosen-measurements on individual copies. We answer this in the negative for arguably the most basic quantum testing problem: deciding whether a given <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> -dimensional quantum state is equal to or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> -far in trace distance from the maximally mixed state. While it is known how to achieve optimal <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(d/\epsilon^{2})$</tex> copy complexity using entangled measurements, we show that with independent measurements, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Omega(d^{4/3}/\epsilon^{2})$</tex> is necessary, even if the measurements are chosen adaptively. This resolves a question posed in [7]. To obtain this lower bound, we develop several new techniques, including a chain-rule style proof of Paninski's lower bound for classical uniformity testing, which may be of independent interest.
Improving software effort estimation using bio-inspired algorithms to select relevant features: An empirical study

Bio-inspired feature selection algorithms got the attention of the researchers in the domain of Software Development Effort Estimations (SDEE) because they can improve the prediction accuracy of existing estimation techniques, such as machine learning methods. This paper aims to analyze different feature selection algorithms and assess the role they can play to increase the accuracy of software development effort predictions. We have performed an empirical study considering commonly used bio-inspired feature selection algorithms in the domain of SDEE, i.e., Genetic Algorithm (GA), Particle Swarm Optimization, Ant Colony Optimization, Tabu Search, Harmony Search (HS), and Firefly algorithm, and four traditional non-bio-inspired algorithms, i.e., Best-First Search (BFS), Greedy Stepwise, Subset Forward Selection, and Random Search, used in combination with five widely used estimation techniques and applied to eight widely used SDEE datasets. The performed analysis suggests that almost all (bio-inspired) feature selection algorithms have outperformed the baseline estimation techniques (i.e., techniques employed without any feature selection algorithms) in the majority of the experiments and hence we can conclude that feature selection algorithms can help in the domain of SDEE to increase the prediction accuracy. Similarly, HS and GA are considered as best performed bio-inspired algorithms because they provided significantly better results than the non-bio-inspired algorithms in a greater number of experiments. Moreover, we also compared the results of various employed bio-inspired algorithms, and, again, GA and HS came out as the best performed bio-inspired feature selection algorithms. From our results, if we have to pick feature selection algorithms (from both bio- and non-bio-inspired) and recommend them for future investigations, we would suggest HS because it provided better effort predictions in more combinations of datasets and estimation techniques than the other considered bio- and non-bio-inspired algorithms. Among the non-bio-inspired algorithms, BFS is the one that provided better predictions.
Estimation of the Optimum Energy Received by Solar Energy Flat-Plate Convertors in Greece Using Typical Meteorological Years. Part I: South-Oriented Tilt Angles

The optimal solar radiation received on an inclined surface is always critical for energy purposes at a location or in an area. Therefore, many attempts have been made worldwide to calculate the optimum tilt angle for this purpose. The present study gives an answer about the south-oriented inclination or inclinations of solar panels in Greece for maximum efficiency. The analysis shows that an angle of 25° (and 30° in some cases) facing south is the most appropriate. To calculate this, the energy sums received on surfaces with inclination angles of 0–60° with a step of 5°, including φ° (φ being the geographical latitude) facing south at 33 locations in Greece were analyzed monthly, seasonally, and annually. The solar radiation data used in this work comes from corresponding typical meteorological years (TMYs) generated for the above locations. TMYs are used for the first time worldwide for the study of the optimum energy received by solar panels tilted south. Four new energy zones are defined to cover the whole of Greece.
Unsupervised random forests

Abstract sidClustering is a new random forests unsupervised machine learning algorithm. The first step in sidClustering involves what is called sidification of the features: staggering the features to have mutually exclusive ranges (called the staggered interaction data [SID] main features) and then forming all pairwise interactions (called the SID interaction features). Then a multivariate random forest (able to handle both continuous and categorical variables) is used to predict the SID main features. We establish uniqueness of sidification and show how multivariate impurity splitting is able to identify clusters. The proposed sidClustering method is adept at finding clusters arising from categorical and continuous variables and retains all the important advantages of random forests. The method is illustrated using simulated and real data as well as two in depth case studies, one from a large multi‐institutional study of esophageal cancer, and the other involving hospital charges for cardiovascular patients.
Accelerating Robot Dynamics Gradients on a CPU, GPU, and FPGA

Computing the gradient of rigid body dynamics is a central operation in many state-of-the-art planning and control algorithms in robotics. Parallel computing platforms such as GPUs and FPGAs can offer performance gains for algorithms with hardware-compatible computational structures. In this letter, we detail the designs of three faster than state-of-the-art implementations of the gradient of rigid body dynamics on a CPU, GPU, and FPGA. Our optimized FPGA and GPU implementations provide as much as a 3.0x end-to-end speedup over our optimized CPU implementation by refactoring the algorithm to exploit its computational features, e.g., parallelism at different granularities. We also find that the relative performance across hardware platforms depends on the number of parallel gradient evaluations required.
Solar extinction map in Chile for applications in solar power tower plants, comparison with other places from sunbelt and impact on LCOE

Direct normal solar irradiation is a crucial parameter for site selection and design of solar power tower plants. It also has a high impact on economic studies, such as the levelized cost of electricity (LCOE). However, direct solar irradiation can be partially extinguished in its path between heliostats and receivers. Therefore, considering only direct normal solar irradiation in the solar resource assessment for tower plant projects is insufficient and leads to errors. This paper presents an improved methodology to estimate the extinction of radiation in the first 150 m of the lower atmosphere, where solar power tower plants are located. This work also shows the first atmospheric extinction maps, which in this case are elaborated for Chilean territory, and the intercomparison of extinction values with other places of interest in the sunbelt. Chile stands out for having the lowest annual atmospheric extinction values, below 4% for 1 km of slant range, while elsewhere annual losses can reach up to 17%. These values limit the amount of local useful solar resource available directly impacting on the LCOE values calculated for solar tower power plants. A new equation for the calculation of the LCOE considering atmospheric extinction is proposed in this paper.
Measurement of mixed-mode fracture characteristics of an epoxy-based adhesive using a hybrid digital image correlation (DIC) and finite elements (FE) approach

The present work seeks to investigate the semi-circular beam (SCB) geometry for studying mixed-mode (mode-I and -II) fracture of a rubber-toughened epoxy-based adhesive material. A hybrid experimental-numerical technique involving digital image correlation technique and finite element modeling is formulated for extracting the J-integral values and stress intensity factors (SIF). Experiments are conducted to compare the results extracted using the hybrid approach with those from regression analysis of measured crack tip displacements. Critical stress intensity factors are extracted for a range of mode mixities. Based on a curve fit of the test data, the mode I and mode II critical stress intensity factors are estimated to be 1.19 MPa/√m and 1.46 MPa/√m, respectively. The specimen geometry, loading configuration and experimental setup are critically assessed to identify and understand effects and outcomes associated with friction at the supports and unintended support asymmetry on stress intensity factors. Several different techniques are also discussed for mapping measured displacement data onto the finite element model for reliably extracting the fracture parameters. Recommendations are made regarding best practices for using this geometry and the associated hybrid technique for extracting fracture parameters.
Cloud Computing: Comparison and Analysis of Cloud Service Providers-AWs, Microsoft and Google

The cloud computing refers to network that enables to distribute processing, application, storage capabilities among many remote located computer systems. In cloud computing platform the IT resources are utilized and released as per the requirement by using internet. It is a better option to organizations and ordinary users to utilize services (IaaS, PaaS, SaaS, DaaS etc) as provided by cloud service providers and need pay as use. Currently a large number of service providers in market and due to diversity of features and services, it very difficult to find suitable provider for s for long term needs. As per market share top three providers are Amazon, Microsoft and Google. In this paper we will analysis some of the tools such as compute, storage space management and performance offered by AWS, Azure and GCP which are the top three market leaders in cloud computing technology. In this paper, we will summarize and compare the features of AWS, Azure & GCP to provide help to organizations and users to choose the suitable features which will fulfill the long term requirements of the users.
Research on Short Text Classification Based on TextCNN

Abstract The TextCNN model is widely used in text classification tasks. It has become a comparative advantage model due to its small number of parameters, low calculation, and fast training speed. However, training a convolutional neural network requires a large amount of sample data. In many cases, there are not enough data sets as training samples. Therefore, this paper proposes a Chinese short text classification model based on TextCNN, which uses back translation to achieve data augment and compensates for the lack of training data. The experimental data shows that our proposed model has achieved good results.
Navigating uncharted waters: Designing business models for virtual and augmented reality companies in the medical industry

New technologies are at the heart of industry transformation. Virtual and augmented reality companies provide fundamentally new ways of communication, treatment, education, and specialist training within the medical industry. However, business models for new ventures that target the medical industry have received scant attention within academic research. Using a multiple case study approach, we analyze how virtual and augmented reality firms create value for their customers in the medical industry. In all, we have studied eight companies that offer different types of solutions for their target segments. The results of the analysis are four design elements consisting of twelve positions and three design themes that define the similarities and differences between the business models for the companies. We contribute to existing research within the field by analyzing business models of the investigated companies using a design approach, classifying the virtual and augmented reality companies, and analyzing the role of new technology in the development of the medical industry.
Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment

The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images. Then, to improve the quality prediction performance, the support vector machine regression (SVR) technique is employed on the features generated by fused networks. In the paper, several promising network architectures are introduced, investigated, and experimentally compared with state-of-the-art NR-IQA methods on two representative MRIQA benchmark datasets. One of the datasets is introduced in this work. As the experimental validation reveals, the proposed fusion of networks outperforms related approaches in terms of correlation with subjective opinions of a large number of experienced radiologists.