Reviewerly/exHarmony huggingface dataset:
A Study of Quality Metrics in Agile Software Development

Traditional waterfallChakravarty, Krishna or iterative waterfall methods are being used in software industries since its birth.Singh, Jagannath In the last decade, time to market and flexibility in accommodating customer requirements became very important aspects in software project delivery. Keeping these two driving factors in mind, agile methodology became very popular, where continuous cycle of development and testing involving high customer interaction is promoted and this practice results in an early time to market. Hence, the quality aspects for these projects become very important in today’s context. Quality metrics play an important role in software engineering as they transform both the customer expectation and operational performance to metrics which can be measured, evaluated and compared. These metrics focus primarily on three aspects: the quality aspects of the product, the process which was followed during its development and some important project parameters. These metrics are influenced by different factors such as functionality, usability, reliability and portability. This research work focuses on literature survey on different quality metrics used in traditional software development, their applicability in agile method and finally searches for the right kind of quality metrics which can be used in agile projects.
Interactive Corridor-Based Path Planning for Teleoperated Driving

Teleoperated driving, where an operator remotely takes over for some time-span, enables a fallback mode for autonomous vehicles. Complex urban scenarios, however, involve a too high workload for the operator. The main reasons for this are the communication delay and the lack of three-dimensional perception. This contribution presents a supervisory control concept with distinct human-machine interactions that explicitly addresses highly complex and sensitive driving tasks. By integrating automated driving functions and human-predefined corridors, a corridor-based planning scheme based on a shared autonomy approach is derived. Within the specified corridor, a modified Constrained CHOMP algorithm determines a collision-free path online using LiDAR sensor information. Since the operator remains outside the closed control loop, the proposed approach is insensitive w.r.t. any time delays. Real driving and simulation experiments confirm the effectiveness of the proposed approach and highlight both the achieved driving safety and the real time capability.
Causality-Aware Neighborhood Methods for Recommender Systems

The business objectives of recommenders, such as increasing sales, are aligned with the causal effect of recommendations. Previous recommenders targeting for the causal effect employ the inverse propensity scoring (IPS) in causal inference. However, IPS is prone to suffer from high variance. The matching estimator is another representative method in causal inference field. It does not use propensity and hence free from the above variance problem. In this work, we unify traditional neighborhood recommendation methods with the matching estimator, and develop robust ranking methods for the causal effect of recommendations. Our experiments demonstrate that the proposed methods outperform various baselines in ranking metrics for the causal effect. The results suggest that the proposed methods can achieve more sales and user engagement than previous recommenders.
A Light Transformer For Speech-To-Intent Applications

Spoken language understanding (SLU) systems can make life more agreeable, safer (e.g. in a car) or can increase the independence of physically challenged users. However, due to the many sources of variation in speech, a well-trained system is hard to transfer to other conditions like a different language or to speech impaired users. A remedy is to design a user-taught SLU system that can learn fully from scratch from users' demonstrations, which in turn requires that the system's model quickly converges after only a few training samples. In this paper, we propose a light transformer structure by using a simplified relative position encoding with the goal to reduce the model size and improve efficiency. The light transformer works as an alternative speech encoder for an existing user-taught multitask SLU system. Experimental results on three datasets with challenging speech conditions prove our approach outperforms the existed system and other state-of-art models with half of the original model size and training time.
Computer based diagnosis of Leukemia in blood smear images

Leukemia also known as blood cancer, mainly consisting of white blood cell (WBC) which damages blood and bones of any being. This disease can be deadly if not identified at an earlier stage. The manual method for detection of leukemia involves many kind of lab tests which are time consuming. The quick and sudden method for the detection of Acute Lymphoblastic Leukemia(ALL) has been proposed in this paper. This method section out the different type of cells from blood smear images such as- White blood cells (WBC), Red blood cells(RBC) and Platelets. l n the next step, the infected cells are distinguished from the normal ones. The implemented method takes blood sample image as one of many acquired data set images for pre-processing and segmentation to find out if the subject has been infected or not. Shape features are obtained to distinguish between lymphoblast and lymphocytes for quick analyses of cancer cells.
An enhancement framework based on gradient domain tone mapping and fuzzy logical for X-ray image of complex workpiece

X-ray images of complex workpiece often suffer low brightness and contrast in industrial defect inspection. In this paper, we propose an enhancement framework to improve the visual quality. In detail, a logarithm transformation is first to increase global brightness, and then, to increase local contrast, we propose a novel gradient domain tone mapping operator, in which we design a new gradient attenuation function based on fuzzy entropy and construct a new cost function constrained by a hybrid regularization. Finally a fuzzy enhancement operator is performed on the result obtained by minimizing the new cost function, to further increase contrast and prevent halo artifacts created by manipulating the gradient domain. Experiments show that the proposed method produces good visual effect for defect inspection for X-ray image of complex workpiece.
Detection of HTTPS Brute-Force Attacks with Packet-Level Feature Set

This paper presents a novel approach to detect brute-force attacks against web services in high-speed networks. The prevalence of brute-force attacks is so high that service providers, such as ISPs or web-hosting providers, cannot depend on their customers' host-based defenses. Moreover, the rising usage of encryption makes it more difficult to detect attacks on the network level. In our research, we created a dataset, which consists of 1.8 million extended IP flows from a backbone network combined with IP flows generated with three popular open-source brute-forcing tools. We identified a distinctive packet-level feature set and trained a machine-learning classifier with a false positive rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> and a true positive rate (the ratio of discovered attacks) of 0.938. The achieved results surpass the state-of-the-art solutions and show that the developed HTTPS brute-force detection algorithm is viable for production deployment.
A novel deep auto-encoder considering energy and label constraints for categorization

Deep auto-encoder (DAE) is one of the representative deep learning algorithms for feature extraction. However, it often shows relatively poor generalization performance to express data without considering the probability distribution of data. Additionally, it cannot be directly applied to classification, because label information is ignored in DAE to judge the given categories. To tackle these issues, in this paper, we propose an energy and label constrained DAE (ELDAE) by integrating energy and label constraints to improve the feature extraction ability of network for classification. Specifically, as the probability distribution for fitting data can be reflected by energy of a network, the energy constraint is designed in this study to improve the probability of ELDAE for fitting data, and make a better expression to data. Moreover, the label constraint is integrated in ELDAE using label information to describe categorization rule, contributing to enhancing the accuracy of classification. We first give the complexity analysis of ELDAE, which is crucial to the property of speed. To exhibit the performance of the proposed ELDAE, we perform comprehensive experiments on benchmark USPS and MNIST datasets, and parameter sensitivity analysis is then provided to investigate the effects of three key parameters including the balance coefficients of weight decay, energy constraint and label constraint. In addition, we compare ELDAE with six state-of-the-art algorithms including Auto-Encoder (AE), Sparse AE (SAE), Deep AE (DAE), Deep Belief Network (DBN), Noisy AE (NAE) and Semi-supervised AE (SSAE). The comparative experimental results demonstrate that ELDAE performs better than the other six competitors in terms of classification accuracy, and keeps the same order of magnitude in terms of training time and testing time.
Unlocking digital literacy practices of EFL teachers

Abstract Digital literacy practice is necessary for education since information and communication technology has developed rapidly, resulting in shifting to the era of digital technology. The digital literacy practices in school and especially in classrooms are required for preparing 21 st -century students. This study aims to (1) determine teachers’ perceptions of digital literacy practices in the level of classroom and school, (2) point out the digital literacy practices of English Foreign Language (EFL) teachers, and (3) investigate the challenges faced by the EFL teachers in applying digital literacy practices in the classroom. This study revealed the digital literacy practices of thirteen EFL Teachers in Yogyakarta using questionnaires and semi-structured interviews. The results uncovered that digital literacy was highly supported, evidenced by the facility, equipment, and the connectivity provided in all schools with a variety of free internet access. In the classroom level, these teachers practised digital literacy by using some digital tools. In implementing digital literacy, the teachers faced some challenges, such as defective equipment and Internet access and intrinsic factors such as teachers’ knowledge in technology, their attitudes toward digital technology, and students’ commitment to apply digital literacy. These findings are followed by recommendations for schools, teachers, and further research.
The Evolution of Empirical Methods in Software Engineering

Empirical methods like experimentation have become a powerful means to drive the field of software engineering by creating scientific evidence on software development, operation, and maintenance, but also by supporting practitioners in their decision-making and learning. Today empirical methods are fully applied in software engineering. However, they have developed in several iterations since the 1960s. In this chapter we tell the history of empirical software engineering and present the evolution of empirical methods in software engineering in five iterations, i.e., (1) mid-1960s to mid-1970s, (2) mid-1970s to mid-1980s, (3) mid-1980s to end of the 1990s, (4) the 2000s, and (5) the 2010s. We present the five iterations of the development of empirical software engineering mainly from a methodological perspective and additionally take key papers, venues, and books, which are covered in chronological order in a separate section on recommended further readings, into account. We complement our presentation of the evolution of empirical software engineering by presenting the current situation and an outlook in Sect. 4 and the available books on empirical software engineering. Furthermore, based on the chapters covered in this book we discuss trends on contemporary empirical methods in software engineering related to the plurality of research methods, human factors, data collection and processing, aggregation and synthesis of evidence, and impact of software engineering research.
Self‐training with one‐shot stepwise learning method for person re‐identification

Summary Person re‐identification (Re‐ID) aims at identifying the same person across multiple non‐overlapping camera views. A number of existing methods have been presented for this task in a fully‐supervised manner that requires a large amount of training annotations. However, obtaining high quality labels is extremely time consuming and expensive. In this article, we focus on the semi‐supervised person Re‐ID and propose a one‐shot stepwise learning method to address the above issue. It exploits only one labeled data along with additional unlabeled samples to gradually but steadily improving the discriminative capability of the feature representation. Specifically, we first construct labeled data portion to train Re‐ID model. Then we fine‐tune the overall system by the following two steps iteratively: (1) assigning the estimated labels to the unlabeled portion; (2) updating the network parameters according to the selected data. During the propagation process, different from conventional sampling method, we propose a novel dynamic sampling strategy to enlarge the pseudo‐labeled subset step by step to make the pseudo labels more reliable. On Market‐1501, DukeMTMC‐ReID and MARS datasets, we conducted extensively experiments to demonstrate that our proposed method contributes indispensably and achieves a very competitive Re‐ID performance.
Azimuth Angle Estimation Based on Sound Wave Reflection for Mirrors and Transparent Objects

Man-made environments contain many mirrors and transparent objects, but it is difficult for robots to recognize transparent objects. To solve this issue, we use sound waves to recognize such hard-to-see objects. This research aimed to enable a robot to wipe a transparent window by estimating the azimuth angles of the window. To achieve this, we use a model-free learning method based on Support Vector Regression (SVR) to capture the features of the sound reflected from the target plane. To determine the input sound signal for the SVR, we derive a sound reflection model based on Shape-from-Shading in the computer vision field. Following this model, we use a sound property in the frequency domain recorded by a microphone as the input to the SVR. As a result of experiments using a transparent plate, in an anechoic chamber, we were able to estimate the azimuth angle within less than 3 degrees. As an example of a robot application using the method, we developed a robot wiping system that can handle such a transparent window. Even with such a realistic environment for the sensor system, we were able to estimate the azimuth angle within almost 5 degrees.
Curve25519 based lightweight end-to-end encryption in resource constrained autonomous 8-bit IoT devices

Abstract Robust encryption techniques require heavy computational capability and consume large amount of memory which are unaffordable for resource constrained IoT devices and Cyber-Physical Systems with an inclusion of general-purpose data manipulation tasks. Many encryption techniques have been introduced to address the inability of such devices, lacking in robust security provision at low cost. This article presents an encryption technique, implemented on a resource constrained IoT device (AVR ATmega2560) through utilizing fast execution and less memory consumption properties of curve25519 in a novel and efficient lightweight hash function. The hash function utilizes GMP library for multi-precision arithmetic calculations and pre-calculated curve points to devise a good cipher block using ECDH based key exchange protocols and large random prime number generator function.
Batch Bayesian Active Learning For Feasible Region Identification by Local Penalization

Identifying all designs satisfying a set of constraints is an important part of the engineering design process. With physics-based simulation codes, evaluating the constraints becomes considerable expensive. Active learning can provide an elegant approach to efficiently characterize the feasible region, i.e., the set of feasible designs. Although active learning strategies have been proposed for this task, most of them are dealing with adding just one sample per iteration as opposed to selecting multiple samples per iteration, also known as batch active learning. While this is efficient with respect to the amount of information gained per iteration, it neglects available computation resources. We propose a batch Bayesian active learning technique for feasible region identification by assuming that the constraint function is Lipschitz continuous. In addition, we extend current state-of-the-art batch methods to also handle feasible region identification. Experiments show better performance of the proposed method than the extended batch methods.
Upper bounds for Rao distance on the manifold of multivariate elliptical distributions

As a natural intrinsic measure on the manifold of probability distributions, Rao distance has received a lot of attentions and been applied successfully to many fields. However, the closed form of Rao distance on the manifold of multivariate elliptical distributions (MEDs) is still absent. In this paper, a class of Manhattan distances (MHDs) with single parameter on MED manifold is constructed by deducing explicit expressions of the geodesic and Rao distance on a specified submanifold of MED manifold. Then, three MHDs in such class, namely ordinary MHD, minimal MHD and quasi-minimal MHD, are provided as upper bounds of Rao distance on MED manifold by specifically, optimally and sub-optimally specifying parameter respectively. The second one can be efficiently computed numerically owing to its convexity with respect to the scalar parameter, and the third one has an explicit form. The performance of the proposed MHDs for approximating Rao distance is illustrated by examples. An application to distributed estimation fusion based on MHD for dynamic system with heavy-tailed process and observation noises is provided.
Anonymity Analysis of Bitcoin, Zcash and Ethereum

As an innovative type of decentralized model, blockchain is a growing list of blocks linked by cryptography. Blockchain incorporates anonymity protocol, distributed data storage, consensus algorithm, and smart contract. The anonymity protocols in blockchain are significant in that they could protect users from leaking their personal information. In this paper, we will conduct a detailed review and comparison of anonymity protocols used in three famous cryptocurrencies, namely Bitcoin, Zcash, and Ethereum.
Blue Noise Plots

Abstract We propose Blue Noise Plots, two‐dimensional dot plots that depict data points of univariate data sets. While often one‐dimensional strip plots are used to depict such data, one of their main problems is visual clutter which results from overlap. To reduce this overlap, jitter plots were introduced, whereby an additional, non‐encoding plot dimension is introduced, along which the data point representing dots are randomly perturbed. Unfortunately, this randomness can suggest non‐existent clusters, and often leads to visually unappealing plots, in which overlap might still occur. To overcome these shortcomings, we introduce Blue Noise Plots where random jitter along the non‐encoding plot dimension is replaced by optimizing all dots to keep a minimum distance in 2D i. e., Blue Noise. We evaluate the effectiveness as well as the aesthetics of Blue Noise Plots through both, a quantitative and a qualitative user study. The Python implementation of Blue Noise Plots is available here.
Walking With Attention: Self-Guided Walking for Heterogeneous Graph Embedding

Heterogeneous graph embedding aims at learning low-dimensional representations from a graph featuring nodes and edges of diverse natures, and meanwhile preserving the underlying topology. Existing approaches along this line have largely relied on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">meta-paths</i> , which are by nature hand-crafted and pre-defined transition rules, so as to explore the semantics of a graph. Despite the promising results, defining meta-paths requires domain knowledge, and thus when the test distribution deviates from the priors, such methods are prone to errors. In this paper, we propose a self-learning scheme for heterogeneous graph embedding, termed as self-guided walk (SILK), that bypasses meta-paths and learns adaptive attentions for node walking. SILK assumes no prior knowledge or annotation is provided, and conducts a customized random walk to encode the contexts of the heterogeneous graph of interest. Specifically, this is achieved via maintaining a dynamically-updated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">guidance matrix</i> that records the node-conditioned transition potentials. Experimental results on four real-world datasets demonstrate that SILK significantly outperforms state-of-the-art methods.
Event-Driven Multi-algorithm Optimization: Mixing Swarm and Evolutionary Strategies

Researchers in nature-inspired optimization have recently proposed multi-population asynchronous algorithms that split the evolutionary process between different search paradigms working in collaboration. These algorithms execute the optimization strategy by reading streams of messages containing solution populations from message queues. After searching for a small number of iterations, new evolved populations are generated and sent back to a queue. Current research suggests that when we have many population-processing algorithms communicating in parallel, parameters intensifying exploration or exploitation in each population strike a dynamic that balances the two, exploring and exploiting simultaneously, maintaining an overall diversity, and improving the search. In this work, we propose a simple reactive migration, population-generation, and processing method for the asynchronous processing of multi-population, multi-strategy algorithms that achieves an improvement over homogeneous configurations. We evaluate this method by comparing a heterogeneous ensemble of multi-populations against a homogeneous solution consisting of a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) populations, using five problems from the noiseless BBOB toolbox for the optimization of continuous functions. Results show that compared with other asynchronous homogeneous population-based algorithms, this method offers better performance concerning the maximum number of evaluations needed to find a solution and the number runs where it found it.