Reviewerly/exHarmony huggingface dataset:
A validated model for the scoping process of quality requirements: a multi-case study

Abstract Quality requirements are vital to developing successful software products. However, there exist evidence that quality requirements are managed mostly in an “ad hoc” manner and down-prioritized. This may result in insecure, unstable, slow products, and unhappy customers. We have developed a conceptual model for the scoping process of quality requirements – QREME – and an assessment model – Q-REPM – for companies to benchmark when evaluating and improving their quality requirements practices. Our model balances an upfront forward-loop with a data-driven feedback-loop. Furthermore, it addresses both strategic and operational decisions. We have evaluated the model in a multi-case study at two companies in Sweden and three companies in The Netherlands. We assessed the scoping process practices for quality requirements and provided improvement recommendations for which practices to improve. The study confirms the existence of the constructs underlying QREME. The companies perform, in the median, 24% of the suggested actions in Q-REPM. None of the companies work data-driven with their quality requirements, even though four out of five companies could technically do so. Furthermore, on the strategic level, quality requirements practices are not systematically performed by any of the companies. The conceptual model and assessment model capture a relevant view of the quality requirements practices and offer relevant improvement proposals. However, we believe there is a need for coupling quality requirements practices to internal and external success factors to motive companies to change their ways of working. We also see improvement potential in the area of business intelligence for QREME in selecting data sources and relevant stakeholders.
Single perceptron at 12 GigaOPs based on a microcomb for versatile, high-speed scalable, optical neural networks

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical microcombs. This approach is programmable and scalable and is capable of reaching ultrahigh speeds. We demonstrate the basic building block ONNs, a single neuron perceptron, by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or GigaOPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten digit recognition and cancer cell detection, achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off the shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.
Learning path recommendation based on Transformer reordering

In order to solve the problem that course learning resources cannot be accurately recommended, this paper proposes learning path recommendation based on transformer reordering. This study constructs the map of knowledge points in the curriculum in advance. When the learners request to learn new knowledge points, the DINA cognitive model is used to analyze the test results, and the blind area knowledge points are obtained and the map of blind area knowledge points is constructed. Then, RankNet algorithm based on neural network is used to realize the first sorting of knowledge points in blind area, and then the personalized characteristics of learners are introduced into transformer algorithm to reorder. Finally, the recommender sequence is combined with the map of knowledge points in the blind area, and the recommended sequence is generated by topological sorting method. In this study, knowledge model and transformer algorithm are used to make up for the deficiency of Curriculum Resource Recommendation problem in previous studies. Taking high school chemistry knowledge points recommendation as an example, the experimental results show that the satisfaction of this method reaches 82%. This recommendation method promotes the research process of personalized learning resources to a certain extent.
Lazy Learning Associative Classification with WkNN and DWkNN Algorithm

O ne of the algorithms, which prudently denote better outcomes than the traditional associative classification systems, is the Lazy learning associative classification (LLAC), where the processing of training data is delayed until a test instance is received, whereas in eager learning, before receiving queries, the system begins to process training data. Traditional method assumes that all items within a transaction is same, which is not always true. This paper recommends a new framework called lazy learning associative classification with WkNN (LLAC_WkNN) which uses weighted kNN method with LLAC, that gives a subset of rules when LLAC is applied to the dataset. In order to predict the class label of the unseen test case, the weighted kNN (WkNN) algorithm is then applied to this generated subset. This creates the enhanced accuracy of the classifier. The WkNN also gives an outlier more weight. By applying Dual Distance Weight to LLAC named as LLAC_DWkNN, this limitation of WkNN is resolved. LLAC_DWkNN gives less weightage to outliers, which improve the accuracy of the classifier, further. This algorithm has been applied to different datasets and the experiment results demonstrate that the proposed method is efficient as compared to the traditional and other existing systems.
Fuzzy Logic and Their application in Different Areas of Engineering Science and Research : A Survey

This Paper give introduction to Fuzzy Logic and Various application in different branch of Engineering and Research. Fuzzy Logic is Applied branch of Mathematics in which Lots of research is taking place. Fuzzy Logic works on "if.....then.....".Fuzzy Logic gives the idea that members are not restricted to T or F definition. The concept of Fuzzy Logic is used in the various fields as Artificial Intelligence, Aerospace, Automotive Business, Defence, Electronics, Finance, Industrial Sector, Marine Sector, Manufacturing, Medical, Securities, Transportation Pattern Recognition and Classification, Psychology, etc. The whole field of Engineering as civil Engineering, Mechanical Engineering, Industrial Engineering real Reliability theory, Robotics, Computer Engineering is broad to be covered here in a comprehensive way.
Multiphenomenology explosion monitoring (MultiPEM): a general framework for data interpretation and yield estimation

SUMMARY An underground nuclear explosion (UNE) couples mechanical energy into crustal rock, which propagates as seismic and acoustic waves. These different physical phenomena transport, by different pathways, to standoff detectors at varying distances. The transport pathways attenuate the original signal but in different ways. Enabled by correct statistical weighting, signal attenuation models can be used to combine these disparate sensor data to estimate the yield of an UNE. Contemporaneous statistical models, used in yield estimation, can be improved with an advanced partition of error for these physical signal propagation models. We present an advanced multivariate approach to error modelling of multiphenomenology physical signatures. In addition to measurement error, our error model represents physical model biases as random with a physics-based covariance structure. To illustrate this proposed framework, we demonstrate the estimation of explosion yield using openly available seismic and acoustic data from chemical single-point explosions.
Performance Analysis of Machine Learning Algorithms Over a Network Traffic

The machine learning algorithms are becoming more potential in artificial intelligence. In order to evaluate network security and performance, the classification of network is very much essential. In recent decades, various software or products produce various network traffic with different parameter types and with different service as per necessity. Thereby, classification of the network traffic plays a major role to increase the performance of a network management. The traffic classification has become more vital study range especially with the evolution of machine learning techniques. Here, we considered few machine learning techniques like the support vector machine, random forest classifier, and multilayer perceptron which are used to classify the network traffic. We considered various parameters like Telnet, Ping, and Voice traffic flows simulated by distributed Internet traffic generator tool. Each host in the network was connected through an overlay network to an Open vSwitch (OVS). The OVS was connected to a Ryu controller which collected basic flow statistics between hosts. These statistics were then parsed by a Python traffic classification script which periodically output the learned traffic labels of each flow. After investigating different classification techniques, we found that multilayer perceptron was found to work much better than other classifiers techniques.
A Method to Compensate for the Errors Caused by Temperature in Structured-Light 3D Cameras

Although low cost red-green-blue-depth (RGB-D) cameras are factory calibrated, to meet the accuracy requirements needed in many industrial applications proper calibration strategies have to be applied. Generally, these strategies do not consider the effect of temperature on the camera measurements. The aim of this paper is to evaluate this effect considering an Orbbec Astra camera. To analyze this camera performance, an experimental study in a thermal chamber has been carried out. From this experiment, it has been seen that produced errors can be modeled as an hyperbolic paraboloid function. To compensate for this error, a two-step method that first computes the error and then corrects it has been proposed. To compute the error two possible strategies are proposed, one based on the infrared distortion map and the other on the depth map. The proposed method has been tested in an experimental scenario with different Orbbec Astra cameras and also in a real environment. In both cases, its good performance has been demonstrated. In addition, the method has been compared with the Kinect v1 achieving similar results. Therefore, the proposed method corrects the error due to temperature, is simple, requires a low computational cost and might be applicable to other similar cameras.
Public-Private Partnership Within the Context of Digital Transformation: Increasingly Larger Role of Educational Institutions

This paper deals with studying the innovative trends in forming the mechanism of public-private partnership (PPP) in education in the Russian Federation, considering global trends. We identified the special aspects of using PPP as a tool in educational activities under certain conditions. We considered various approaches to developing the PPP models and illustrated the relevant mechanism involving a public corporation and an engineering university. Viable models were proposed for the interrelations of businesses, authorities, and educational institutions beyond the known schemes; we also justified their feasibility and showed how PPP activities affect forming the contents of engineering education, enhancing its quality, increasing the qualifications of academic staff, activating academic mobility, and developing international contacts and interuniversity relations. The authors are proving the necessity of involving the authorities in PPP to support the educational institutions of additional professional education (APE) and solving the strategical problems of developing the real-sector enterprises. We analyzed the key aspects of PPP in education, exemplified by a specific university. The findings can be widely used to perform applied studies on the relevant topics, while the models developed can be further reproduced.
Looking to the future of the cyber security landscape

Last year marked a turning point in the industry as organisations accelerated their digital transformation efforts. The move towards more cloud-based tools and remote working has been dominating the conversation over recent years, but in 2020 this need was kicked into overdrive for many organisations – it was a case of innovate or be left behind. The move towards more cloud-based tools and remote working has dominated the conversation in recent years. But in 2020 this need was kicked into overdrive. The acceleration of digital transformation has reinforced the importance of cyber security and brought into focus the need to manage and secure against remote devices and threats. Rashid Ali of Wallix explains how businesses need to ensure they have complete control and visibility of all company data, while supporting employee productivity.
Adaptive learning cost‐sensitive convolutional neural network

Real-world classification often encounters a problem called class imbalance. When the data of some classes are redundant than that of other classes, traditional classifiers usually bias their decision boundaries to the redundant majority classes. Most proposed cost-sensitive strategies often ignore the hard-to-learn examples or have a large amount of hyper-parameters. This article proposes an adaptive learning cost-sensitive convolutional neural network to solve this problem. During the training process, the proposed method embeds a class-dependent cost to each class in the global error, making the decision boundary bias to the minority classes. Meanwhile, a distribution weight is assigned to each example to enhance the learning of the hard-to-learn examples. Both the class-dependent costs and distribution weights are learnt automatically in the net. This cost-sensitive approach makes the algorithm focus on the examples in the minority classes as well as the hard-to-learn examples in each class. Besides, this approach can be applied to both binary and multi-class image classification problems without any modification. Experiments are conducted on four image classification datasets to evaluate this algorithm. The experimental results show that the proposed method achieves better performance than the baseline algorithms and some other algorithms.
Set-valued data collection with local differential privacy based on category hierarchy

&lt;abstract&gt; &lt;p&gt;Set-valued data is extremely important and widely used in sensor technology and application. Recently, privacy protection for set-valued data under differential privacy (DP) has become a research hotspot. However, the DP model assumes that the data center is trustworthy, consequently, increasingly attention has been paid to the application of the local differential privacy model (LDP) for set-valued data. Constrained by the local differential privacy model, most methods randomly respond to the subset of set-valued data, and the data collector conducts statistics on the received data. There are two main problems with this kind of method: one is that the utility function used in the random response loses too much information; the other is that the privacy protection of the set-valued data category is usually ignored. To solve these problems, this paper proposes a set-valued data collection method (SetLDP) based on the category hierarchy under the local differential privacy model. The core idea is to first make a random response to the existence of the category, continue to disturb the item count if the category exists, and finally randomly respond to a candidate itemset based on the new utility function. Theory analysis and experimental results show that the SetLDP can not only preserve more information, but also protect the category private information in set-valued data.&lt;/p&gt; &lt;/abstract&gt;
An extended DEIM algorithm for subset selection and class identification

The discrete empirical interpolation method (DEIM) has been shown to be a viable index-selection technique for identifying representative subsets in data. Having gained some popularity in reducing dimensionality of physical models involving differential equations, its use in subset-/pattern-identification tasks is not yet broadly known within the machine learning community. While it has much to offer as is, the DEIM algorithm is limited in that the number of selected indices cannot exceed the rank of the corresponding data matrix. Although this is not an issue for many data sets, there are cases in which the number of classes represented in a given data set is greater than the rank of the data matrix; in such cases, it is impossible for the standard DEIM algorithm to identify all classes. To overcome this issue, we present a novel extension of DEIM, called E-DEIM. With the proposed algorithm, we also provide some theoretical results for using extensions of DEIM to form the CUR matrix factorization in identifying both rows and columns to approximate the original data matrix. Results from applying variations of E-DEIM to two different data sets indicate that the presented extension can indeed allow for the identification of additional classes along with those selected by standard DEIM. In addition, comparing these results to those of some more familiar methods demonstrates that the proposed deterministic E-DEIM approach including coherence performs comparably to or better than the other evaluated methods and should be considered in future class-identification tasks.
An insulator inspection method based on deep learning applicable to multi‐scale and occlusion conditions

As an important equipment of power system, the insulator's normal operation is the basis to ensure the safe operation of the power system. The insulator positioning and identification technology based on machine vision can quickly and accurately complete the inspection of insulators on site and effectively save the cost of operation and maintenance. This paper proposes an insulator inspection method based on region-convolutional neural networks (RCNNs). First, the dataset of the insulator image is preprocessed by means of data expansion. Then, the feature extraction of the insulator image is realised by using the zeiler fergus (ZF) network. The k-means clustering method is used to optimise the selection of anchor points. Meanwhile, the non-maximum suppression post-processing algorithm is improved, and a non-linear penalty factor is introduced to adapt to multi-scale and overlapping occlusion insulator inspection. Experimental results show that the improved faster RCNNs insulator inspection method can accurately obtain the coordinate frame and the corresponding probability value of the insulator object and improve the average precision by 10.43%, achieving the accurate inspection of the insulator object.
MENTAL CARD AS A LEARNING METHOD

The article reveals such a way of using pedagogical technology as a mental map. The possibility of using it as a way to increase the effectiveness of the learning process is being investigated. The authors emphasize that the use of this pedagogical technology is an integral part of education in modern conditions. The technological essence of the mental map is revealed, which is distinguished by originality and effectiveness in active teaching methods. The comprehensive possibilities of using the mental map in educational activities, in particular on the example of the discipline "Geography", are considered. Software for developing mind maps is presented. The experience of using this technology made it possible to reveal that this method is useful for both the teacher and the student. It becomes obvious that the assimilation of theoretical material for freshmen is difficult (the influence of various factors affects), in this regard, the application of this method becomes effective. Individually created mental maps allow you to visually remember the material in the form of mapping. Application of the investigated method allows grouping a large flow of information material, structuring it and managing it. The material depicted schematically contains the main provisions of the topic being studied, replacing numerous words, using only the basic concepts and keywords. Revealed the regulatory and communicative function of the mental map.
Automatic Segmentation of Non-tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional Networks

In brain tumor diagnosis and surgical planning, segmentation of tumor regions and accurate analysis of surrounding normal tissues are necessary for physicians. Pathological variability often renders difficulty to register a well-labeled normal atlas to such images and to automatic segment/label surrounding normal brain tissues. In this paper, we propose a new registration approach that first segments brain tumor using a U-Net and then simulates missed normal tissues within the tumor region using a partial convolutional network. Then, a standard normal brain atlas image is registered onto such tumor-removed images in order to segment/label the normal brain tissues. In this way, our new approach greatly reduces the effects of pathological variability in deformable registration and segments the normal tissues surrounding brain tumor well. In experiments, we used MICCAI BraTS2018 T1 and FLAIR images to evaluate the proposed algorithm. By comparing direct registration with the proposed algorithm, the results showed that the Dice coefficient for gray matters was significantly improved for surrounding normal brain tissues.
Design Choices for Central Bank Digital Currency: Policy and Technical Considerations

Central banks around the world are exploring and in some cases even piloting Central Bank Digital Currencies (CBDCs). CBDCs promise to realize a broad range of new capabilities, including direct government disbursements to citizens, frictionless consumer payment and money-transfer systems, and a range of new financial instruments and monetary policy levers. CBDCs also give rise, however, to a host of challenging technical goals and design questions that are qualitatively and quantitatively different from those in existing government and consumer payment systems. A well-functioning CBDC will require an extremely resilient, secure, and performant new infrastructure, with the ability to onboard, authenticate, and support users on massive scale. It will necessitate an architecture simple enough to support modular design and rigorous security analysis, but flexible enough to accommodate current and future functional requirements and use cases. A CBDC will also in some way need to address an innate tension between privacy and transparency, protecting user data from abuse while selectively permitting data mining for end-user services, policymakers, and law enforcement investigations and interventions.In this paper, we enumerate the fundamental technical design challenges facing CBDC designers, with a particular focus on performance, privacy, and security. Through a survey of relevant academic and industry research and deployed systems, we discuss the state of the art in technologies that can address the challenges involved in successful CBDC deployment. We also present a vision of the rich range of functionalities and use cases that a well-designed CBDC platform could ultimately offer users.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.