Reviewerly/exHarmony huggingface dataset:
Nuts and Bolts of Extracting Variability Models from Natural Language Requirements Documents

Natural language (NL) requirements documents are often ambiguous, and this is considered as a source of problems in the later interpretation of requirements. Ambiguity detection tools have been developed with the objective of improving the quality of requirement documents. However, defects as vagueness, optionality, weakness and multiplicity at requirements level can in some cases give an indicationArganese, Eleonora of possible variability, either in design and in implementation choices or configurability decisions. Variability information is actually the seed of the software engineering development practice aiming at building families of related systems, known as software product lines. Building on the results of previous analyses conducted on large and real word requirementFantechi, Alessandro documents, with QuARS NL analysis tool, we provide here a classification of the forms of ambiguity that indicate variation points, and we illustrate the practical aspects of the approach by means of a simple running example. To provide a more complete description of a line of softwareGnesi, Stefania products, it is necessary to extrapolate, in addition to variability, also the common elements. To this end we propose here to take advantage of the capabilities of the REGICE tool to extract and cluster the glossary terms from the requirement documents. In summary, we introduce the combined application of two different NL processingSemini, Laura tools to extract features and variability and use them to model a software product line.
Energy Efficient Transmission of 3D Meshes Over MMWave-Based Massive MIMO Systems

Many mixed reality applications are based on the real-time compression and streaming of three-dimensional (3D) models. Thus, they demand very high-bandwidth and ultra-low latency from network specifications. The next-generation wireless networks will employ promising technologies to significantly improve the communication data rates. However, due to implementation complexity and thus increased energy consumption of these technologies, a trade-off between the quality-of-user-experience (QoE) and the hardware specifications is necessary. To overcome these limitations low-resolution quantizers have been of interest, which provide a trade-off between quality and complexity. In this paper, we propose a complexity-aware perceptual coding scheme that minimizes the reconstruction losses of the 3D models. Extensive simulations assuming different 3D models show that the proposed scheme achieves plausible reconstruction output offering significantly higher energy efficiency gains, as compared to a context unaware coding approaches.
Measurement Method of Underwater Target Based on Binocular Vision

Traditional measurement methods are obtained by calculating the global disparity map. The accuracy of these methods is low and the speed is slow. Under the condition of uneven illumination, low contrast and obvious noise, they can’t meet the measurement requirements well. An underwater image enhancement method based on Contrast Limited Adaptive Histogram Equalization and image weighting fusion is proposed. For the measurement of underwater target, only two matching points are solved in the algorithm design, which improves the accuracy and speed of underwater ranging. The idea of coarse-to-fine matching is adopted, that is, the SAD algorithm is used in matching area to complete the coarse matching such that the matching windows can be determined firstly. Then the stereo matching algorithm based on NCC and Census fusion is used to decide the final matching point. The algorithm is insensitive to light and can extract edges and corners better. Finally, three-dimensional reconstruction is carried out to restore the three-dimensional coordinates of the endpoints to be measured, and the real size can be obtained by calculating the Euclidean distance between the two points. The experimental results show that the algorithm runs fast and robustly without calculating the disparity of the whole image, and the results meet the accuracy requirements.
Abductive Reasoning in New Venture Ideas

Understanding how new venture ideas emerge and how this process can be augmented by the wealth of data and computing power of artificial intelligence (AI) is critical. Here, we explicate how abduction operates in relation to the generation of new venture ideas and how this might be augmented in a world of AI. Compared to deduction and induction, abduction is a more useful form of reasoning and innovative abduction generates more innovative new venture ideas than explanatory abduction. We further posit that AI will become an enabler for more rapid generation of high quality and more numerous new venture ideas.
Efficient Second-Order Shape-Constrained Function Fitting

We give an algorithm to compute a one-dimensional shape-constrained function that best fits given data in weighted- $$L_{\infty }$$ norm. We give a single algorithm that works for a variety of commonly studied shape constraints including monotonicity, Lipschitz-continuity and convexity, and more generally, any shape constraint expressible by bounds on first- and/or second-order differences. Our algorithm computes an approximation with additive error $$\epsilon $$ in $$O\left( n \log \frac{U}{\epsilon } \right) $$ time, where U captures the range of input values. We also give a simple greedy algorithm that runs in O(n) time for the special case of unweighted $$L_{\infty }$$ convex regression. These are the first (near-)linear-time algorithms for second-order-constrained function fitting. To achieve these results, we use a novel geometric interpretation of the underlying dynamic programming problem. We further show that a generalization of the corresponding problems to directed acyclic graphs (DAGs) is as difficult as linear programming.
Cryptographic adaptation of the middle square generator

&lt;span lang="EN-US"&gt;Currently, cryptography plays a major role in various computer and technological applications. With the high number of internet users, the use of cryptography to provide information security has become a priority. Several applications such as e-mails, electronic banking, medical databases and e-commerce require the exchange of private information. While, if the connection is not secure, this sensitive information can be attacked. The best-known cryptographic systems rely on the generation of random numbers, which are fundamental in various cryptographic applications such as key generation and data encryption. In what follows, we want to use pseudo-random sequences generated by the middle square generator. In this work, it must be possible to estimate that the data produced has random characteristics, knowing that the algorithm used is deterministic. Overall, this paper focuses on the testing of pseudo-random sequences generated by the middle square generator and its use in data encryption.&lt;/span&gt;
DEVELOPMENT OF PERSONAL CULTURAL AND AESTHETIC PERCEPTION BY MEANS OF PERFORMING ART

The paper addresses the problem of development of aesthetic perception in an individual by means of performing art. The potential of performing art and the special aspects of aesthetic perception development are revealed through a case study involving the primary school children. Defined is the concept of ‘aesthetic perception’, as well as the criteria and levels of its development; revealed are the psychological mechanisms of influence of performing art, being poly-artistic at its core, on the development of cultural and aesthetic perception in an individual. Described is the process of diagnosing and shaping aesthetic perception in primary school children through stage and creative activities. Delivered are the results of research on the problem reflecting the positive dynamics revealed in the experimental participants.
From Nondeterministic to Multi-Head Deterministic Finite-State Transducers

Every nondeterministic finite-state automaton is equivalent to a deterministic finite-state automaton. This result does not extend to finite-state transducers - finite-state automata equipped with a one-way output tape. There is a strict hierarchy of functions accepted by one-way deterministic finite-state transducers (1DFTs), one-way nondeterministic finite-state transducers (1NFTs), and two-way nondeterministic finite-state transducers (2NFTs), whereas the two-way deterministic finite-state transducers (2DFTs) accept the same family of functions as their nondeterministic counterparts (2NFTs).We define multi-head one-way deterministic finite-state transducers (mh-1DFTs) as a natural extension of 1DFTs. These transducers have multiple one-way reading heads that move asynchronously over the input word. Our main result is that mh-1DFTs can deterministically express any function defined by a one-way nondeterministic finite-state transducer. Of independent interest, we formulate the all-suffix regular matching problem, which is the problem of deciding for each suffix of an input word whether it belongs to a regular language. As part of our proof, we show that an mh-1DFT can solve all-suffix regular matching, which has applications, e.g., in runtime verification.
A Qualitative Research on Foreign Language Learners’ Perceptions of Facebook as a Learning Environment

Although the use of Facebook in EFL education is relatively new, it may bring a potential to promote communicative and productive skills. This study aims to explore Turkish EFL learners’ perception of Facebook regarding their reasons to use it, perceived harmful effects and the utilization of Facebook as a language learning environment. The study was conducted with 30 preservice EFL teachers studying at the department of English Language Teaching (ELT) at Balikesir University. The study followed a qualitative tradition. The data were gathered through reflections, essay papers and interviews. The findings showed that EFL learners use Facebook for communication and interaction even if they perceive several harmful effects. Moreover, EFL learners regarded Facebook as an educational environment and as a foreign language learning environment. Thus, EFL teachers are recommended to be trained in the use of social media in the foreign language teaching and learning processes. In addition, the curricula of the EFL teacher training programs should include a specific course focusing on topics to raise awareness of the use of Facebook as a language learning environment.
Augmenting and Tuning Knowledge Graph Embeddings

Knowledge graph embeddings rank among the most successful methods for link prediction in knowledge graphs, i.e., the task of completing an incomplete collection of relational facts. A downside of these models is their strong sensitivity to model hyperparameters, in particular regularizers, which have to be extensively tuned to reach good performance [Kadlec et al., 2017]. We propose an efficient method for large scale hyperparameter tuning by interpreting these models in a probabilistic framework. After a model augmentation that introduces per-entity hyperparameters, we use a variational expectation-maximization approach to tune thousands of such hyperparameters with minimal additional cost. Our approach is agnostic to details of the model and results in a new state of the art in link prediction on standard benchmark data.
Group Pruning Using a Bounded- $$\ell _p$$ ℓ p Norm for Group Gating and Regularization

Deep neural networks achieve state-of-the-art results on several tasks while increasing in complexity. It has been shown that neural networks can be pruned during training by imposing sparsity inducing regularizers. In this paper, we investigate two techniques for group-wise pruning during training in order to improve network efficiency. We propose a gating factor after every convolutional layer to induce channel level sparsity, encouraging insignificant channels to become exactly zero. Further, we introduce and analyse a bounded variant of the $$\ell _1$$ regularizer, which interpolates between $$\ell _1$$ and $$\ell _0$$ -norms to retain performance of the network at higher pruning rates. To underline effectiveness of the proposed methods, we show that the number of parameters of ResNet-164, DenseNet-40 and MobileNetV2 can be reduced down by $$30\%$$ , $$69\%$$ , and $$75\%$$ on CIFAR100 respectively without a significant drop in accuracy. We achieve state-of-the-art pruning results for ResNet-50 with higher accuracy on ImageNet. Furthermore, we show that the light weight MobileNetV2 can further be compressed on ImageNet without a significant drop in performance .
A Study on the Way of the Korean Language Education using the Multiple Intelligence Theory in the Era Artificial Intelligence

This study aims to apply the Multiple Intelligence (MI) theory to the Korean Language Education in the 4SUPth/SUP Industrial Revolution and investigate the needs of implementing the MI-based instruction and the way of Korean Language Education. For this purpose, this paper focused on Ethical and Ideological Intelligences and Creative and Instrumental Intelligences out of the MI. Four kinds of suggestion are provided in this paper. First, the students should be given an opportunity to discover and develop their strongest intellectual competences. Second, the teachers should be curriculum expert, counselor and career coach. Third, Korean Language Education should organize the curriculum to enhance students’ creative and convergence competencies using MI. Finally, Korean Language Education should consider the students first and how to teach, instead of what to teach. This is the education paradigm shift in the new era of Artificial Intelligence.
Cloud Based Architecture Design of System of Systems

Infrastructure as code (IaC) does not quite trip off the tongue, and its meaning is not always clear. However, IaC has been with us since the beginning of Development Operations Engineering (DevOps)—and some experts say DevOps would not be possible without it. To remain competitive, digital-first companies must build, deploy, and integrate applications faster than ever. Currently Enterprise Application Integration (EAI) mediation and facade function commonly achieved by ESB solutions, which hard to maintain, evolve and assess operational quality. In this paper, we described perspective direction of System Of Systems (SoS) architecture design responsible for facade and mediation EAI functions based on cloud resources. Outlined major complications and challenges to integrate innovative approaches with computer aided design (CAD), computer aided engineering (CAE) and product lifecycle management (PLM) software. Described industry-wide trends to tackle challenges and market options available. Proposed an approach to evaluate quality of service (QoS) for solution architecture. Compared performance of existing approach with cloud based.
Why Software Development Interviews Are Hard

In this chapter, we’ll start out by examining why so many of us find interviews, especially in software development, to be such a painful and difficult experience. We’ll start out discussing physiological responses and how to manage them. Next, we’ll discuss some strategies for turning the psychological responses into something that is actually useful. Finally, we’ll examine several ways to prepare for interviews that we will discuss in further detail later in the book.
Development and Validation of a Multidimensional Measure of Responsible Business Leadership

The prevailing literature mostly explores responsible leadership in business organizations as a uni-dimensional concept with CSR as focus. Through the present study we take the conceptualization further, by exploring it as a multi-dimensional construct that balances the strategic and behavioral aspects of leadership. Using a strategic leadership lens, we define the first dimension as leaders’ sustainable growth focus and second dimension as display of multi-stakeholder consideration for its’ normative as well as strategic value. Strategy authors such as Ireland and Hitt (1999) suggest that these goal setting dimensions would find value within the organization only when they are hinged leaders’ ethical behaviors with the followers. We address this through the two dimensions viz. moral person and moral manager of normative ethical leadership, as advanced by Trevino, Hartman and Brown (2000). These dimensions focus on the behavioral perspective of responsible leadership. Finally, we create an overall responsible leadership definition representing these four dimensions. Through four quantitative studies, we develop the proposed factor structure, test the construct validity through proving its convergent validity with related leadership styles and discriminant validity with unrelated leadership styles. We further test its ability to explain unique variance in certain key outcomes over and above explained by previous leadership theories. Overall, we suggest that studying the various aspects of leader’s responsibility through responsible leadership would help in establishing its contribution to some unique antecedents and outcomes. Keywords: Responsible leadership, construct validation, leadership development, citizenship behaviors towards stakeholders
Personalized Query Auto-Completion Through a Lightweight Representation of the User Context.

Query Auto-Completion (QAC) is a widely used feature in many domains, including web and eCommerce search, suggesting full queries based on a prefix typed by the user. QAC has been extensively studied in the literature in the recent years, and it has been consistently shown that adding personalization features can significantly improve the performance of QAC. In this work we propose a novel method for personalized QAC that uses lightweight embeddings learnt through fastText. We construct an embedding for the user context queries, which are the last few queries issued by the user. We also use the same model to get the embedding for the candidate queries to be ranked. We introduce ranking features that compute the distance between the candidate queries and the context queries in the embedding space. These features are then combined with other commonly used QAC ranking features to learn a ranking model. We apply our method to a large eCommerce search engine (eBay) and show that the ranker with our proposed feature significantly outperforms the baselines on all of the offline metrics measured, which includes Mean Reciprocal Rank (MRR), Success Rate (SR), Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (NDCG). Our baselines include the Most Popular Completion (MPC) model as well as a ranking model without our proposed features. The ranking model with the proposed features results in a $20-30\%$ improvement over the MPC model on all metrics. We obtain up to a $5\%$ improvement over the baseline ranking model for all the sessions, which goes up to about $10\%$ when we restrict to sessions that contain the user context. Moreover, our proposed features also significantly outperform text based personalization features studied in the literature before, and adding text based features on top of our proposed embedding based features results only in minor improvements.
Using homing, synchronizing and distinguishing input sequences for the analysis of reversible finite state machines

A digital device is called reversible if it realizes a reversible mapping, i.e., the one for which there exist a unique inverse. The ?eld of reversible computing is devoted to studying all aspects of using and designing reversible devices. During last 15 years this ?eld has been developing very intensively due to its applications in quantum computing, nanotechnology and reducing power consumption of digital devices. We present an analysis of the Reversible Finite State Machines (RFSM) with respect to three well known sequences used in the testability analysis of the classical Finite State Machines (FSM). The homing, distinguishing and synchronizing sequences are applied to two types of reversible FSMs: the converging FSM (CRFSM) and the non-converging FSM (NCRFSM) and the effect is studied and analyzed. We show that while only certain classical FSMs possess all three sequences, CRFSMs and NCRF-SMs have properties allowing to directly determine what type of sequences these machines possess.
An End-to-end Auto-driving Method Based on 3D Lidar

Abstract The development of artificial intelligence, especially deep learning engineering technology, has made auto-driving cars more and more realistic. The end-to-end auto-driving method is an automatic driving system which is different from the rule-based system of. It uses the data from the environment to output vehicle control information solutions directly, greatly reducing the system complexity. 3D Lidar is the core sensor of automatic driving system. In this paper, a deep convolution neural network is designed for the end-to-end automatic driving method. This paper uses 64-line 3D Lidar data and transformation algorithm, transforms the 3D Lidar point-cloud data into depth images which can be used directly by an end-to-end deep learning network. This paper matches 3D Lidar data with vehicle-mounted Can bus data to obtain data and tags which will be feed into the deep learning network. The output of the deep learning network is the controlling information that directly acts on the vehicle. Based on experimental verification, the end - to - end automatic driving method based on 3D Lidar is of great value and potential for further development.
A Simple and Efficient Algorithm for Finding Minimum Spanning Tree Replacement Edges

Given an undirected, weighted graph, the minimum spanning tree (MST) is a tree that connects all of the vertices of the graph with minimum sum of edge weights. In real world applications, network designers often seek to quickly find a replacement edge for each edge in the MST. For example, when a traffic accident closes a road in a transportation network, or a line goes down in a communication network, the replacement edge may reconnect the MST at lowest cost. In the paper, we consider the case of finding the lowest cost replacement edge for each edge of the MST. A previous algorithm by Tarjan takes $O(m \alpha(m, n))$ time, where $\alpha(m, n)$ is the inverse Ackermann's function. Given the MST and sorted non-tree edges, our algorithm is the first that runs in $O(m+n)$ time and $O(m+n)$ space to find all replacement edges. Moreover, it is easy to implement and our experimental study demonstrates fast performance on several types of graphs. Additionally, since the most vital edge is the tree edge whose removal causes the highest cost, our algorithm finds it in linear time.
An universal GMMs-based sample strategy and collision checker for robot motion planning

Abstract The sampling-based motion planning methods acquire obstacle information by performing collision detection for a large number of samples, which can effectively solve autonomous obstacle avoidance problem in the high-dimensional configuration space. In order to improve the efficiency of planning, this paper proposes a new adaptive sampling strategy and collision checker based on Gaussian Mixture Models(GMMs): For the "narrow corridor" problem, the sample set obtained by Gaussian sampling strategy of adjustable standard deviation is used to train the GMMs. The models fitting the target area in the manipulator configuration space can guide the adaptive sampling; using the GMMs fitting the obstacle region to predict the collision probability of the samples rapidly, and using Greedy K-means initializes the EM algorithm to improve the fitting accuracy of the model. Finally, the sampling strategy and collision detection method are combined with various sampling-based motion planning algorithms, and multiple simulation experiments are carried out to verify the results. The results show that the proposed method has significantly improved planning efficiency compared with the traditional method.