Reviewerly/exHarmony huggingface dataset:
An Approach to Software Maintenance: A Case Study in Small and Medium-Sized Businesses IT Organizations

Software maintenance is the task of modifying a running product previously delivered to the client, in order to correct defects, improve performance or adapt it to the environment. This task is a crucial activity for enterprises. Without it, existing systems would become rapidly out-of-date and inefficient. The purpose of this paper is to present a software maintenance approach used in small and medium-sized business (SMB) organizations in Brazil. Currently, these organizations represent 95.5% of the software companies in the country. The approach presented here indicates how SMB IT companies have improved their software maintenance processes. Multiple case studies were performed to validate this approach. The outcomes showed that strategies associated with managing users’ knowledge and development/maintenance teams are relevant to increase the maintenance process effectiveness. This approach involves three aspects: users’ knowledge management, maintenance team knowledge and the management and maintenance process. This improvement includes reducing time and also minimizing the number of tickets. The response time for tickets resolution to the end user has been reduced. In addition, IT organizations have minimized the effects associated with both staff and client turnovers.
DCT-Based Local Descriptor for Robust Matching and Feature Tracking in Wide Area Motion Imagery

We introduce a novel discrete cosine transform-based feature (DCTF) descriptor designed for both robustly matching features in aerial video and tracking features across wide-baseline oblique views in aerial wide area motion imagery (WAMI). Our DCTF descriptor preserves local structure more compactly in the frequency domain by utilizing the mathematical properties of the discrete cosine transform (DCT) and outperforms widely used the spatial-domain feature extraction methods, such as speeded up robust features (SURF) and scale-invariant feature transform (SIFT). The DCTF descriptor can be used in combination with other feature detectors, such as SURF and features from accelerated segment test (FAST), for which we provide experimental results. The performance of DCTF for image matching and feature tracking is evaluated on two city-scale aerial WAMI data sets (ABQ-215 and LA-351) and a synthetic aerial drone video data set digital imaging and remote sensing image generation (Rochester Institute of Technology (RIT)-DIRSIG). DCTF is a compact 120-D descriptor that is less than half the dimensionality of state-of-the-art deep learning-based approaches, such as SuperPoint, LF-Net, and DeepCompare, which requires no learning and is domain-independent. Despite its small size, the DCTF descriptor surprisingly produces the highest image matching accuracies ( F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> = 0.76 and ABQ-215), the longest maximum and average feature track lengths, and the lowest tracking error (0.3 pixel, LA-351) compared with both handcrafted and deep learning features.
VisualNeuro: A Hypothesis Formation and Reasoning Application for Multi‐Variate Brain Cohort Study Data

Abstract We present an application, and its development process, for interactive visual analysis of brain imaging data and clinical measurements. The application targets neuroscientists interested in understanding the correlations between active brain regions and physiological or psychological factors. The application has been developed in a participatory design process and has subsequently been released as the free software ‘VisualNeuro’. From initial observations of the neuroscientists' workflow, we concluded that while existing tools provide powerful analysis options, they lack effective interactive exploration requiring the use of many tools side by side. Consequently, our application has been designed to simplify the workflow combining statistical analysis with interactive visual exploration. The resulting environment comprises parallel coordinates for effective overview and selection, Welch's t‐test to filter out brain regions with statistically significant differences and multiple visualizations for comparison between brain regions and clinical parameters. These exploration concepts enable neuroscientists to interactively explore the complex bidirectional interplay between clinical and brain measurements and easily compare different patient groups. A qualitative user study has been performed with three neuroscientists from different domains. The study shows that the developed environment supports simultaneous analysis of more parameters, provides rapid pathways to insights and is an effective tool for hypothesis formation.
A systematic map on verification and validation of emergent behavior in software engineering research

Emergent behavior cannot be attributed to one individual system alone but arises in the interplay of various systems, components etc. Ensuring the correctness of emergent behavior is a well-known challenge that has been addressed by research in various subfields of software engineering. This paper aims at providing a unified view on the research activities conducted and research contributions made on verification and validation of emergent behavior. We have conducted a systematic mapping study on the topic of verification and validation of emergent behavior. We applied a combined search strategy using manual, database, and snowball search. In total we investigated 7211 papers, from these 168 relevant papers have been included and classified. Results show an increasing interest in the topic of verification and validation of emergent behavior. As only little validation and evaluation research has been conducted, the field can be considered still immature. There exist different verification and validation techniques used in the various solution approaches such as model checking, simulation, or runtime monitoring. It stands out that even though research is published in different software engineering fields and subfields no verification or validation technique can be attributed solely to a single field.
Mechanisms for Developing Intercultural Competence - International Experience

Engineers are intensively involved in the integration in the field of science and technology. They participate in various international programs and projects, which imply an increase in international professional contacts and the availability of intercultural competence. Thus, the development of intercultural competence for future engineers is of particular importance. To become more global, it is essential to study the most successful practices in this field. In our research, we would like to demonstrate China's experience, as according to university rankings Chinese universities take leading positions which is mostly due to efficient strategies for fostering intercultural competence. This work aims to study the mechanisms for the intercultural competence development of Chinese students. In our research, intercultural competence is viewed as the key condition for international academic mobility and we concentrated on its importance and benefits for future engineers. Then we gave a detailed analysis of the most efficient mechanisms for intercultural competence development in China.
Local Path Planning for Autonomous Mobile Robot Based on APF-BUG Algorithm with Ground Quality Indicator

In this paper, an enhanced Artificial Potential Field (AFP) algorithm applied to autonomous mobile robot is presented. The proposed solution is extended by an additional BUG algorithm and a ground quality indicator. The modification allows to avoid local minima in path planning caused by complex terrain obstacles. The developed algorithm takes into account the substrate quality, classifying a poor ground as an obstacle. It was implemented and tested in Matlab software, utilizing the track planning algorithm as a state machine. The simulation environment enables graphical presentation of the chosen path and the arrangement of moving area. The article presents and discusses a basic issue in local path planning of autonomous mobile platforms, i.e. the ground quality, which is ignored in classical algorithms. This is a non-trivial problem, which impacts the success rate of getting the final destination. The developed algorithm is extended by a BUG rule and ground quality indicator which allows to avoid immobilization of the platform.
Data-Limited Modulation Classification With a CVAE-Enhanced Learning Model

The modulation classification problem can be effectively handled by modern machine learning techniques, however, the available data for training is usually limited due to costly measurements in practice. Concerning the problem of insufficient training data, we propose a conditional variational auto-encoder (CVAE)-enhanced learning model for modulation classification. Different from the conventional data augmentation approaches where the data is independently generated, we introduce a feedback unit from the classifier to the CVAE generative network, to ensure that the generated data helps improving the classification accuracy. A two-stage generating-training algorithm is proposed. Via both simulation and practical implementation on a universal software radio peripheral (USRP) platform, it is shown that the proposed method effectively improves the classification accuracy in realistic propagation environment.
Hand Tracking Accuracy Enhancement by Data Fusion Using Leap Motion and Myo Armband

In this paper, by using the combination of Leap Motion and Myo armband, two methods for hand tracking and online hand gesture identification are proposed. With the proposed methods, We have improved the measurement accuracy of the palm direction and solved the problem of insufficient accuracy when the palm is at the limit of the measurement range. We use the Kalman filter algorithm and the neural network classification method to process the data measured by Leap Motion and Myo, so that the tracking of the operator's hand gesture is more accurate and robust even when the hand is at positions close to the measurement limit of one single sensor. The methods, which improve the hand tracking accuracy, can be used for robotic control, demonstration or teleoperation. The effectiveness of the proposed methods has been demonstrated through comparative experiments.
Performance Evaluation of Voice Encryption Techniques Based on Modified Chaotic Systems

With the substantial development of communication systems, especially in the public channels through which the knowledge moves, channel problems are growing. The protection of privacy and data is critical and should be improved, and speech scrambling techniques are one branch of IT that has become an important topic in modern life, primarily used in military security applications. In addition to its applications in the civil side. Speech Scrambling is described as altering speech signals to make them incomprehensible to listeners who are not expected to receive the communication. It will therefore make it difficult for eavesdrops or attackers to understand it. The main objective of this paper is to model and simulate speech scrambling's proposed structure models based on chaotic signals. The scrambling method structure consists of three main components: scrambling, noisy channel and descrambling. The scrambling and descrambling processes have an inverse relationship. On the basis of chaotic signals, the Speech Scrambling was examined using Arabic and English Analog speech signals, spoken by a man or woman of different ages. The paper suggests a voice scrambling models which uses four various modified chaotic system: Random Logistic maps RLM, Random Lorenz system RLS, Random Chen system RCS and Random Unified system RUS based on OFDM technique. The performance evaluation measurements contain five different tests: Mean Square Error MSE, Segmental Spectral Signal to Noise Ratio SSSNR, Entropy H, Correlation Coefficient R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> and Bit Error Rate BER. The objective test to measuring a residual intelligibilities R.I. of voice between the original and scrambled signals for proposed models are: SSSNR = -19.5, Rc=0.11, MSE = 0.43 and H=97%, while the BER between the original signal and recovered signal at 16 dB about 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-03</sup> . This result is good compared to other results and according to the references that mentioned in the research. Conducted a series of randomness test for chaotic signals as national institute of standards and technology NIST test.
Uniqueness and Optimality of Dynamical Extensions of Divergences

We introduce an axiomatic approach for channel divergences and channel relative entropies that is based on three information-theoretic axioms of monotonicity under superchannels (i.e. generalized data processing inequality), additivity under tensor products, and normalization, similar to the approach given recently for the state domain. We show that these axioms are sufficient to give enough structure also in the channel domain, leading to numerous properties that are applicable to all channel divergences. These include faithfulness, continuity, a type of triangle inequality, and boundedness between the min and max channel relative entropies. In addition, we prove a uniqueness theorem showing that the Kullback-Leibler divergence has only one extension to classical channels. For quantum channels, with the exception of the max relative entropy, this uniqueness does not hold. Instead we prove the optimality of the amortized channel extension of the Umegaki relative entropy, by showing that it provides a lower bound on all channel relative entropies that reduce to the Kullback-Leibler divergence on classical states. We also introduce the maximal channel extension of a given classical state divergence and study its properties.
Deep feature learning with attributes for cross-modality person re-identification

Cross-modality person re-identification (Re-ID) between RGB and infrared domains is a hot and challenging problem, which aims to retrieve pedestrian images cross-modality and cross-camera views. Since there is a huge gap between two modalities, the difficulty of solving the problem is how to bridge the cross-modality gap with images. However, most approaches solve this issue mainly by increasing interclass discrepancy between features, and few research studies focus on decreasing intraclass cross-modality discrepancy, which is crucial for cross-modality Re-ID. Moreover, we find that despite the huge gap, the attribute representations of the pedestrian are generally unchanged. We provide a different view of the cross-modality person Re-ID problem, which uses additional attribute labels as auxiliary information to increase intraclass cross-modality similarity. First, we manually annotate attribute labels for a large-scale cross-modality Re-ID dataset. Second, we propose an end-to-end network to learn modality-invariant and identity-specific local features with the joint supervision of attribute classification loss and identity classification loss. The experimental results on a large-scale cross-modality Re-ID benchmarks show that our model achieves competitive Re-ID performance compared with the state-of-the-art methods. To demonstrate the versatility of the model, we report the results of our model on the Market-1501 dataset.
Visualization Analysis of Seismic Facies Based on Deep Embedded SOM

As a classical visualization tool for seismic facies analysis, the clustering process of self-organizing map (SOM) is generally divided into two stages: feature extraction and clustering. However, when the horizon pickings are ambiguous and waveforms are chaotic, the structural feature extraction cannot correspond well to the topological structure of SOM. To improve the performance of classification, we propose a one-stage method of deep embedded SOM (DESOM) for seismic facies visualization analysis, which means that the extracting feature representation and clustering are completed simultaneously. Moreover, the stability of the DESOM results can be improved by adding sparse constraints, and thus the visualization results can be displayed in more detail. In the experiment, by comparing with the external indexes of clustering and the hierarchical results of prototype categories, the superiorities of the DESOM and sparse DESOM (SDESOM) methods are verified based on a geophysical model. In the field data application, these methods are combined with the Hue, Saturation, Value (HSV) color mapping technology to display the geological structure information of the target horizon. According to the law of the correlation of facies and the internal cluster indexes, it approves that the DESOM method can improve the continuity of channels, and the SDESOM method can obtain more detailed information of seismic facies distribution.
Deep-learned faces: a survey

Abstract Deep learning technology has enabled successful modeling of complex facial features when high-quality images are available. Nonetheless, accurate modeling and recognition of human faces in real-world scenarios “on the wild” or under adverse conditions remains an open problem. Consequently, a plethora of novel deep network architectures addressing issues related to low-quality images, varying pose, illumination changes, emotional expressions, etc., have been proposed and studied over the last few years.This survey presents a comprehensive analysis of the latest developments in the field. A conventional deep face recognition system entails several main components: deep network, optimization loss function, classification algorithm, and train data collection. Aiming at providing a complete and comprehensive study of such complex frameworks, this paper first discusses the evolution of related network architectures. Next, a comparative analysis of loss functions, classification algorithms, and face datasets is given. Then, a comparative study of state-of-the-art face recognition systems is presented. Here, the performance of the systems is discussed using three benchmarking datasets with increasing degrees of complexity. Furthermore, an experimental study was conducted to compare several openly accessible face recognition frameworks in terms of recognition accuracy and speed.
De‐motivators for the adoption of agile methodologies for large‐scale software development teams: An SLR from management perspective

Abstract Most of the software development projects have traditionally been faced with severe problems in terms of quality, cost, and time. Researchers and practitioners have focused on agile software development methods (ASDMs) as an alternative to overcome these problems. Agile methods employ iterative development cycles, interspersed by user feedback. Agile methods were basically developed for small development teams. Scaling agile methods is a big issue from different perspectives. De‐motivators play a key role in project management as it allows early identification and prompt management of threats that may arise during project execution. The objective of this paper is to identify the de‐motivators while scaling agile at large, from management perspectives. We have adapted SLR and applied contrived search criteria derived from the research questions, followed by selecting the required research papers, data extraction, and data synthesis, which resulted in 15 de‐motivators from 58 relevant papers. Some of the identified de‐motivators are ‘traditional organizational culture’, ‘lack of agile experts’, ‘reluctance to adopt’, and ‘lack of management and commitment support’. The identified factors have been compared from various perspectives, such as continents, digital libraries, organization size, and so forth.
Overview of the role of artificial intelligence in pathology: the computer as a pathology digital assistant

Pathology is rapidly evolving as it begins to embrace artificial intelligence (AI) as an enabling technology. In addition to the basic concepts of machine learning, it is important to understand the overall framework in which digital pathology and computational pathology are creating tremendous opportunities for advancing 21st century diagnostics. From a discussion of machine learning modalities, it will be shown that both now and in the foreseeable future, the computer will not replace human cognition, creativity, and imagination. AI, however, will become a tireless and accurate pathologist's assistant.
How Implicit Assumptions on the Nature of Trust Shape the Understanding of the Blockchain Technology

Abstract The role that trust plays in blockchain-based systems is understood and portrayed in various manners. The blockchain technology is said to enable and establish trust as well as to redirect it, to substitute for it, and to make it obsolete. Furthermore, there is disagreement on whom or what users have to trust when using the blockchain technology: (only) code, math, algorithms, and machines, or still (also) human actors. This paper hypothesizes that the divergences of the depictions largely rest on implicitly adhering to different accounts of trust. Thus, the goal of this paper is to outline how the current lack of a shared understanding of the term “trust” leads to diverging interpretations of the blockchain technology’s core features. Furthermore, it shows how this lack of common understanding obstructs scholars from referring to one another meaningfully in the discourse on blockchain technology. To do so, this paper outlines the most prominent depictions of the setup of relevant trust relationships within blockchain-based systems and traces their roots to different underlying assumptions on the nature of trust.
Toward precision health: applying artificial intelligence analytics to digital health biometric datasets

The rapid development of digital health devices has enabled patients to engage in their care to an unprecedented degree and holds the possibility of significantly improving the diagnosis, treatment and monitoring of many medical conditions. Combined with the emergence of artificial intelligence algorithms, biometric datasets produced from these digital health devices present new opportunities to create precision-based, personalized approaches for healthcare delivery. For effective implementation of such innovations to patient care, clinicians will require an understanding of the types of datasets produced from digital health technologies; the types of analytic methods including feature selection, convolution neural networking, and deep learning that can be used to analyze digital data; and how the interpretation of these findings are best translated to patient care. In this perspective, we aim to provide the groundwork for clinicians to be able to apply artificial intelligence to this transformation of healthcare.
Dynamical 2D and 3D image encryption method by hybrid system based on cat map and wavelet transform

In this paper, a novel random sequence generator based on cat map and hybrid chaos system is introduced. The features of the new system are investigated by various tests as NISTS test. As an example of the application of this system, a new image encryption algorithm based on the transformation matrix that is generated by this system is introduced. The proposed system has dynamically structured, so that by changing the input values, it creates a new system. Then the encryption algorithm has a dynamic structure such that a new system is used for each image. And this factor increases the security of the encryption algorithm. Then the proposed algorithm is extended to color, multiple and 3D images. Also, the security of the proposed algorithm is examined by using various tests.