Reviewerly/exHarmony huggingface dataset:
An Infrastructure for Platform-Independent Experimentation of Software Changes

Current experimentation platforms for online controlled experimentation focus on the technical execution of an experiment. This makes them specific to the application domain, the expected infrastructure, and the used technology. Moreover, the experiment definitions include numerous implicit assumptions about the platform's implementation. As a result, experiments are difficult to replicate or compare across platforms or even platform versions.
Algorithm Modeling of Smile Intensity Estimation Using a Spatial Attention Convolutional Neural Network

Abstract Smile intensity estimation is a challenging task as it required subtle feature extraction, self-adapted weighted model and classifier. complexity of the problem domains, and problems on fine-grained image recognition are some of the issues related to intensity estimation. In this study, we designed a self-weighted deep convolutional neural network architecture for smiles intensity estimation using graphics processing unit. In the case of using only CK+ smile images, the accuracy of the model is also higher than that of the latest technology. Our model achieved better accuracy by just using CK+ smile images than state-of-the-art techniques. Visualizations of learned features at various layers and their deconvolutions are also presented for understanding the learning process.
The Dark Side of Licensing Cryptocurrency Exchanges as Payment Institutions

The ultimate objective of cryptocurrencies is to become a payment system substituting, complementing, or competing with the existing conventional fiat-based payment systems. Irrespective of whether such an objective could be accomplished, the functional similarities between certain cryptocurrencies and fiat money has persuaded competent authorities of certain EU Member States to grant payment institution licenses to cryptocurrency exchanges. At first blush, granting such an authorization would seem to be a step forward as it would bring otherwise unregulated cryptocurrency exchanges within the scope of the existing payment regulatory framework. However, such an authorization not only faces major legal challenges related to the definition of a payment institution, but also introduces new lesser-known risks. Aside from the semantic and definitional issues, authorizing cryptocurrency exchanges as payment institutions can bring activities and instruments - with a different risk profile than that of conventional payment instruments - within the scope of payment systems. It appears that such risks embedded in those instruments cannot be fully addressed under the existing payment laws.This paper studies two examples of unattended risks under the cryptocurrency-exchange-as-payment-institution regime. The first risk concerns the use of untethered, non-convertible, illiquid and volatile settlement assets for settlement purposes in cryptocurrency exchanges. The second risk concerns the risks associated with finality of settlements arising from the use of probabilistic finality in some of the most popular cryptocurrency blockchains. Given that in the conventional payment institutions central bank money or commercial bank money is primarily used as the settlement asset, such risks have already been addressed or otherwise taken for granted, however, in cryptocurrency exchanges, the risks involved in the settlement of liabilities with an illiquid and volatile asset relying on probabilistically final settlement mechanism cannot be dealt with by the existing applicable regulations. As the risks cannot be addressed within the current European payment regulation framework, an alternative policy option would be granting a special license to cryptocurrency businesses or introducing ring-fencing mechanism to protect the conventional payment systems from the risks of cryptocurrency payments.
Toward software artifacts ecosystem

In the process of developing and maintaining a software product, many things are created and used that are called software artefacts. Software artifacts are created, changed, reused, and change relationships in the development and maintenance processes of a software product. The complexity and variety of software artifact relationships require adequate means of description and management. They may be a software artifacts ecosystem. In the article, for the first time, a concept of a software artifact ecosystem is proposed. The concept describes a generic model of the software artifacts ecosystem, which is the Cornerstone ecosystem type and consists of three actors — the platform, the software, and the artifact. Based on the generic model, the SD model of the software artifacts ecosystem is described. The roles of actors in the ecosystem are indicated, the relationships between actors are described. The developer's activities will be more efficient, the software is understandable, and the development and maintenance is cheaper when the styles (standards) are used. As case study, based on the generic model of the software artifacts ecosystem, a declarative model of the programming style ecosystem has been developed. Three-level model of programming style artifact is proposed. The tools and processes for creating and using a programming style artifact are developed and described.
Predicting defects in object-oriented software using cost-sensitive classification

Abstract In this software era, it is vital to produce reliable and good quality software. Early detection of defects aids in building accurate software with reduced cost and other resources. Researchers have a keen interest in producing machine learning models for effective and accurate software defect prediction in the early stages of software development. Object-oriented metrics of the software are used in developing these models. These models may result in biased predictions owing to the class imbalance problem existing in most of the software datasets. This paper provides an effective defect prediction framework for imbalanced data by employing cost-sensitive classifiers and stable performance measures like GMean, Balance, and AUC. Four decision tree-based classifiers with different cost ratios are investigated to predict defects in three Apache projects. The empirical results are statistically validated using the nonparametric Friedman test and Wilcoxon signed-rank test. The results state with 99% confidence that the predictive capability of J48, AdaBoostM1, Bagging, and RandomSubSpace improved after employing cost-sensitive learning for the four classifiers used in this study.
Model Compression on Faulty Array-based Neural Network Accelerator

Due to an increase in the size of Deep Neural Networks (DNNs), special purpose hardware has gained prominence to accelerate the forward pass of the network like Google's Tensor Processing Unit (TPU) and Eyeriss. The heart of these accelerators is a Matrix Multiplication unit, which is based on systolic array architecture. This array processor has a grid-like structure, made of individual Processing Elements (PEs) that can be extended along row and column. A lot of work has been done on the computing array implementation and its reliability concerns in the past. However, their fault tolerance perspective with respect to DNNs is not yet fully understood with a fault model. We, in this paper, first present a fault model i.e., different sequences in which faults can occur on the array. We classify the fault modes into random, row, and column, and study their impact on the accuracy of DNNs followed by overheads of the mitigation strategies. Pruning is a process of removing redundant parameters in the model to decrease the network size for efficient performance. Although several pruning techniques have been developed to reduce the inference time on general-purpose and special-purpose systems, model compression (pruning) under faulty scenario has not yet been explored. In the second part of our work, we co-design a Fault based and Array size based Pruning (FPAP) algorithm with an intent of bypassing the faults and removing the internal redundancy at the same time for efficient inference. We compare our method with recent pruning methods under different fault scenarios and array sizes. We achieve a mean speedup of 4.2x where the baselines achieved 1.6x on ConvNet, NiN, AlexNet, VGG16 over Eyeriss in the case of random faults.
A Widely Analysis &amp; Assessment of Software Deformity Prone Datasets Models using LinearNNsearch Classification Model

In software world, researcher work is always focusing on number of faults present in a software deformity prone datasets model. The main goal of researcher work is that to find and fix present faults in software deformity prone as soon as possible. This has much to do with software deformity prone datasets models and its accuracy growth. Currently world immense software companies, they are used their own accuracy repository system which handling the software deformity prone issues and controlled the accuracy of deformity prone datasets models. This research paper presents the current issues on software deformity prone datasets models and the significant method used on these issues. Our research model method is that to boost the accuracy of forecasts in software deformity prone based on LinearNNsearch Classification method. This method used with K parameters where K=N=1 to 6. The experimental analyses showed that the parameter K=N=3, 4 and 5 are good for linearNNsearch and can enhanced the positive accuracy of software deformity prone with linearNNsearch. The analysis experiments of IBK Filtered neighbor search at K=N=5, 6 can also increase the positive accuracy of software deformity prone.
Calculating altmetrics based on the data obtained at users’ accessing library e-catalogs

The authors examine the libraries’ independent calculations and analysis of alternative metrics to determine user-defined relevancy of publications. Logfiles collected by web-servers at users’ accessing e-catalogs may be used for such calculations and analysis. The almetrics’ key advantages as compared to traditional bibliometrics and webometrics are: real-time representation; openness and transparency; coverage of wider non-academic audience; coverage of various sources and research findings. The web-server log-files enable to analyze number of user views of bibliographic records in e-catalog, to rate authors, individual publications and topical queries for a time period. In case of links to full texts, the number of pdf. documents loaded by users and n umber of p ages viewed ( when library computer-based system supports full-text document scrolling mode) may be defined. The authors also discuss specificity of handling log-files for various analysis types: statistical, content addressable, defining sequential sets, clustering. The functionality and table structure of RNPLST’s software module for advanced bibliometric analysis is described. The further task of the study will be to define trends of user behavior and forecasts for user group behavior when using e-catalog. The article is prepared within the framework of the State Order to Russian National Public Library for Science and Technology “Development and improvement of the system of Open Archive of integrated information and library resources of Russian National Public Library for Science and Technology as modern knowledge management system in digital environment: on the way to Open Science” № 075-01300-20-00 for 2020–2022.
User Experience Goals for Cognitive Systems in Smart Business Buildings

Tenant experience in a smart business building is shaped both by the technical solutions available in the building and by human stakeholders at different touch points in the building context. We have been applying design-thinking approach to study what kinds of experiences should smart business buildings provide to the users, especially to the tenants of the building. We conclude our findings as user experience vision and nine user experience goals for the tenants of smart business buildings. Furthermore, we analyse how the current stakeholders of the building contribute to each targeted experience, and suggest how cognitive systems could enhance those experiences.
Locating Visual Explanations for Video Question Answering

Although promising performance has been reported for Video Question Answering (VideoQA) in recent years, there is still a large gap for human to truly understand the model decisions. Besides, beyond a short answer, complementary visual information is desirable to enhance and elucidate the content of QA pairs. To this end, we introduce a new task called Video Question Answering with Visual Explanations (VQA-VE), which requires to generate answers and provide visual explanations (i.e., locating relevant moments within the whole video) simultaneously. This task bridges video question answering and temporal localization. They are two separate and typical visual tasks and come with our challenge. For training and evaluation, we build a new dataset on top of ActivityNet Captions by annotating QA pairs with temporal ground-truth. We also adopt a large-scale benchmark TVQA. Towards VQA-VE, we develop a new model that is able to generate complete natural language sentences as answers while locating relevant moments with various time spans in a multi-task framework. We also introduce two metrics to fairly measure the performance on VQA-VE. Experimental results not only show the effectiveness of our model, but also demonstrate that additional supervision from visual explanations can improve the performance of models on traditional VideoQA task.
IoT network traffic analysis: Opportunities and challenges for forensic investigators?

As IoT devices become more incorporated into our daily lives, their always on approach makes them an ideal source of evidence. While these devices should use encryption to protect sensitive information, in reality this is not always the case e.g. some expose sensitive data like credentials in cleartext. In this paper, we have conducted an extensive analysis on the communications channels of 32 IoT consumer devices. Our experiments consisted of four main parts; first we carried out a port scan to determine if any ports can be exploited and thus gain remote access. Second, we looked at whether any of the devices used encryption and if not what type of content was exposed. Third, we used the network traffic ‘metadata’ to identify the destination the data terminated. Finally, we examined the communication between the mobile app and the cloud to see if it can be easily exploited using a proxy server. Our findings show that the majority of devices have remote access unavailable. We found the Shannon entropy test a useful pre-test in identifying unencrypted content. Although many devices encrypted their data, we found several in particular smart cameras would send data in cleartext when they detected motion or during updates. We found the majority of data transverses to the US and stored on Amazon servers with most devices contacting multiple destination. Lastly, we discovered many of the IoT device's mobile apps can be easily exploited using a HTTP Proxy.
Enhanced Microseismic Event Detection Using Deep Neural Networks

Summary A reliable automatic event detection procedure is a vital tool in any passive seismic monitoring scenario, where recordings often span long periods of time and events are typically below the noise level. A commonly-used automatic detection procedure involves taking the ratio of a Short-Time Average to Long-Time Average (STALTA). This approach has two pitfalls: its high susceptibility to noise, and, its highly sensitive parameters which need careful tuning. Whilst a number of alternatives have been proposed they are often too computationally heavy or time consuming to provide an alternative for realtime detection. This work adapts a common procedure from the field of computer vision to develop and train a deep learning model to automatically detect seismic events in a passive monitoring scenario. Trained on synthetic data, the proposed approach requires no manual annotation of data - a time-consuming task. Benchmarked against the STALTA approach on a recorded event observed in the North Sea, the deep learning model was shown to accurately detect the event even on the noisy traces where the STALTA failed. With a compute time faster than the data flow, the proposed approach is a strong contender for realtime monitoring applications.
Variadic equational matching in associative and commutative theories

In this paper we study matching in equational theories that specify counterparts of associativity and commutativity for variadic function symbols. We design a procedure to solve a system of matching equations and prove its termination, soundness, completeness, and minimality. The minimal complete set of matchers for such a system can be infinite, but our algorithm computes its finite representation in the form of solved set. From the practical side, we identify two finitary cases and impose restrictions on the procedure to get an incomplete algorithm, which, based on our experiments, describes the input-output behavior and properties of Mathematica's flat and orderless pattern matching.
Is It Still Worth Challenging the Status Quo? A Design Process Article With an Example Application to Australian Jockey’s Safety Vests

Horse racing is a highly dangerous activity that imposes the compulsory wearing of jockeys’ safety vests. Although “design thinking” has gained popularity in many fields (e.g., business, health, information technology, education), product innovation is still not used widely in the design of some of the personal protective equipment available to jockeys. This article discusses about an Australian design case study on jockeys’ safety vests that used a qualitative research approach along with user experience design principles, which led to consider a revision of this framework to accommodate design dependencies in terms of a suggested dependency-based user experience design framework. Hence, this article calls for further research in this field.
A Recipe of Capabilities for Pursuing Expertise in Data Visualization: A Practitioner's Perspective

Data visualization is hard to master because of the inherent complexities that characterize the challenge of facilitating understanding. Competence with data visualization is gaining in recognition as an essential capability and thus fostering the necessary skills is paramount to prepare students for their future professional activity in this field; yet, it is a challenge for educators to design programs that cover all facets. This article presents a framework that profiles the range of different capability “ingredients” which form the recipe of expertise in data visualization, from the point of view of an experienced practitioner.
Multiscale stick-breaking mixture models

Abstract Bayesian nonparametric density estimation is dominated by single-scale methods, typically exploiting mixture model specifications, exception made for Pólya trees prior and allied approaches. In this paper we focus on developing a novel family of multiscale stick-breaking mixture models that inherits some of the advantages of both single-scale nonparametric mixtures and Pólya trees. Our proposal is based on a mixture specification exploiting an infinitely deep binary tree of random weights that grows according to a multiscale generalization of a large class of stick-breaking processes; this multiscale stick-breaking is paired with specific stochastic processes generating sequences of parameters that induce stochastically ordered kernel functions. Properties of this family of multiscale stick-breaking mixtures are described. Focusing on a Gaussian specification, a Markov Chain Monte Carlo algorithm for posterior computation is introduced. The performance of the method is illustrated analyzing both synthetic and real datasets consistently showing competitive results both in scenarios favoring single-scale and multiscale methods. The results suggest that the method is well suited to estimate densities with varying degree of smoothness and local features.
Enforce a Global Security Policy for User Access to Clustered Container Systems via User Namespace Sharing

With the advancement of containerization technologies and the isolation mechanisms provided by the Linux kernel through features like namespaces and cgroups, a question arises whether total isolation in containers (virtual enclave) can provide an increased level of security in all use cases. In the current paper we aim to explore the idea of unifying the container's user namespace with the host system's user namespace, to validate if this approach may increase the overall security in some areas of use. Such an approach can facilitate the implementation of complex access policies with high granularity and reduce the weak points that can lead to privilege-escalation attacks. We will explore how different containerization engines can be configured to support the user namespace unification and we will see why the Singularity containerization engine is a perfect fit for our purposes. In addition, we will propose a concept architecture for an academic cluster that can natively support the enforcement of a unified user access policy among both: underlying nodes and containers running above.
Detection of pulse signal in chaotic noise background using extreme learning machine

Abstract It is still a challenge to detect the useful signals under chaotic noise background with effective methods. Difficults such as suppression of useful signals, large computation and low sensitivity commonly exist the new method based on the nonlinear characteristics of signals, such as neural network method, and traditional methods. On the contrast, The extreme learning machine has advantages of strong nonlinear approximation, simple structure, high precision, fast learning and training speed, etc. On this basis, this paper proposes a method of utilizing extreme learning machine based on least square to get the output weight, in order to train the model to detect the weak pulse signal.
Segmented Approach to Path Planning

A* algorithm performs well as a Best First Search method, which would not give the shortest path in certain scenarios. Its accuracy depends on the heuristic function and has slow processing speed in the real world. RRT performs slower than A* and Dijkstra's algorithm gives correct output but shows us a slow runtime performance unsuitable for the real-world. This paper uses Dijkstra's algorithm using the priority queue for testing and proposes an approach that can be applied to any path planning algorithm. Experimental results show that the proposed approach performs 51% faster than A* on game datasets and 14% faster on extremely dense map datasets.
Research status of damage identification algorithm based on deep learning

One of the core tasks of computer vision is target detection. With the rapid development of deep learning, target detection technology based on deep learning has become the mainstream algorithm in this field. As one of the main application fields, damage identification has achieved important development in the past decade. This paper systematically summarizes the research progress of damage identification algorithm based on deep learning, analyzes the advantages and disadvantages of each algorithm and its detection results on voc2007, voc2012 and coco data sets. Finally, the main contents of this paper are summarized, and the research prospect of deep learning based damage identification algorithm is prospect.