Reviewerly/exHarmony huggingface dataset:
Phase unwrapping method based on parallel local minimum reliability dual expanding for large-scale data

Considering data reliability, the optimized connections of dual residues for phase discontinuity reconstruction provide a more reliable scheme and produce more robust unwrapped results. However, their practical implementation usually involves a time-consuming iterative global operation, which is not suitable for application to the phase unwrapping (PU) of big blocks of interferometric synthetic aperture radar (InSAR) phase data. A parallel PU method based on local minimum reliability dual expanding is proposed. With given quality weight maps, the dual reliability is defined based on residues, and the minimum reliability residue pairs are introduced to represent possible discontinuous boundaries. We provide a dynamic expanding method for the pairs with local minimum reliability searching and dual merging. The final minimum balanced trees obtained are used for path integrating of PU with the aid of reliability maps. The calculation of reliability maps, the residue pair searching, and the dynamic expanding are designed to be carried out in parallel. We employ the interface propagation scheme based on the eikonal equation and flood-filling for the parallel implement. Two big blocks of airborne InSAR data were processed by the proposed method, the experimental results and analysis verified its robustness and efficiency for the large-scale PU problem.
Counteracting Agile Retrospective Problems with Retrospective Activities

Retrospective meetings are a fundamental part of Agile software development methods. Effective retrospectives can positively impact teamwork, productivity, and work satisfaction. In this paper, we focus on problems that commonly occur during these meetings. To address them, we suggest retrospective activities that are already used in practice. These activities provide structure and guide the team through the meeting. We created a mapping between a selection of these activities and the problems they attempt to solve. We evaluated the created mapping through multiple case studies with software development teams in educational and professional contexts. Our results verify the existence of a relationship between specific retrospective activities and connected retrospective problems. Furthermore, using observational studies we could verify parts of the created problem-activity mapping.
On the Use of Quality Models to Characterize Trustworthiness Properties

Making informed choices when designing or contracting a system is yet a very challenging task. One of the biggest users’ concern is to select the most trustworthy solution. However, it is difficult to understand the trustworthiness of a system, because it encompasses a large diversity of properties such as security, privacy, performance, among others. Composing a measure that considers such a large number of properties, the relationship among them and their relevance in the composition requires a well defined model, such as a quality model. In this experience report, we study whether quality models can provide scores that are useful to characterize those properties, helping users to choose the most trustworthy of the available alternatives. Then, we have chosen a property that is on the top of users concerns: data privacy. Results showed a higher percentage of success of linkage attacks when the privacy score is lower, indicating the usefulness of quality models in measuring and improving data privacy and providing interesting insights to the users.
Exploring non-emissive wearable display as a clothing accessory

In this paper, we explore wearable ambient display as a clothing accessory. A majority of wearable display research so far focuses on using displays as a channel to present concrete information and notifications. The aim of our research is to develop an understanding and identify the preferred characteristics of wearable displays as an everyday accessory and embellishment. We designed the Scarf Set headphones in the form of a hooded scarf, as an instance to explore the design space. The Scarf Set is used as a probe to provoke conversation around wearable displays as an everyday clothing and accessory item. The initial user study shows that our participants appreciated the interactive features enabled by the wearable display, but the constant changing and movement, which are part of the interaction, were not liked.
An Improved Two-Step Face Recognition Algorithm Based on Sparse Representation

Weighted score fusion is a widely used score fusion scheme, but the weights need to be set manually. The results generally vary greatly when the weights are different, so it is difficult to find the optimal weights. This is why it is necessary to constantly set different weights for experimental comparisons to find the optimal weights. In this paper, an improved fusion method is proposed for above shortcoming, that is, multiplication fusion applicable to sparse representation. The fusion scheme not only is easy to use but also does not need to be artificially set weights. Moreover, it is consistent with the correlation between the classification error and the score obtained by the experimental analysis. In the field of face recognition, it has been shown that the two-step face recognition (TSFR) based on representation using the original training samples and the generated “symmetric face” training samples can achieve excellent face recognition performance. Face recognition based on multiplication fusion and TSFR proposed in this paper can further improve the recognition accuracy.
Tensor-based type-2 random vector functional link network

In the paper, random vector functional link (RVFL) network is extended by the tensor structure, and the enhancement nodes are replaced by type-2 fuzzy sets. Two versions of RVFL network are proposed, one is the interval type-2 RVFL network, and the other is the tensor-based type-2 RVFL network. The interval type-2 RVFL network is similar to the original RVFL, the only difference is that the outputs of enhancement nodes are the defuzzification results of the uncertainty weight method. The tensor-based type-2 RVFL network adopts the defuzzification results of the uncertainty weight method, together with values of the lower membership functions and values of upper membership functions, a 4-dimensional tensor is then formed based on the three components; then, Moore-Penrose inverse of even-order tensor which is implemented via Einstein product is used for solving the tensor equation. Moreover, the weighting vector is obtained via a balance weighting factor, which is resulted from a weighted average of the weighting vector of RVFL network and the weighting vector of the solution to the abovementioned tensor equation. Finally, the proposed algorithms are verified on three nonlinear benchmark functions, a nonlinear system identification problem, and four regression problems.
Predictive Uncertainty Estimation with Temporal Convolutional Networks for Dynamic Evolutionary Optimization

Prediction strategies in dynamic evolutionary optimization aim at estimating the moving optimum after a change of the fitness function. Considering the predicted optimum for re-initialization of the population, the evolution strategy is led into the direction of the next optimum. We propose a new way to control the influence of the prediction depending on its estimated uncertainty. In addition, we construct a new benchmark generator for dynamic optimization problems, Dynamic Sine Benchmark, tailored to prediction approaches. For prediction of the moving optimum and uncertainty estimation we apply a temporal convolutional network (TCN) with Monte Carlo dropout. In the experimental study, we compare our approach to known prediction and re-initialization strategies. The results show the advantage of the new re-initialization strategy and TCNs with uncertainty estimation for complex problems up to a certain dimensionality.
Similar physical entity matching strategy for mobile edge search

In recent years, a large number of intelligent sensing devices have been deployed in the physical world, which brings great difficulties to the existing entity search. With the increase of the number of intelligent sensing devices, the accuracy of the search system in querying the entities to match the user's request is reduced, and the delay of entity search is increased. We use the mobile edge technology to alleviate this problem by processing user requests on the edge side and propose a similar physical entity matching strategy for the mobile edge search. First, the raw data collected by the sensor is lightly weighted and expressed to reduce the storage overhead of the observed data. Furthermore, a physical entity matching degree estimation method is proposed, in which the similarity between the sensor and the given sensor in the network is estimated, and the matching search of the user request is performed according to the similarity. Simulation results show that the proposed method can effectively reduce the data storage overhead and improve the precision of the sensor search system.
Attacking Speaker Recognition Systems with Phoneme Morphing

As voice interfaces become more widely available they increasingly implement speaker recognition, to provide both personalized functionalities and security via authentication. In this paper, we present a method that transforms the voice of one person so that it resembles the voice of a victim, such that it can be used to deceive speaker recognition systems into believing an utterance was spoken by the victim. The transformation only requires short pieces of audio recordings from the source and victim voices, and does not require specific words to be spoken by the victim. We show that the attack can be improved by using a population of source voices and we provide a metric to identify promising source voices, from within such a population. We evaluate our attack along a set of dimensions, including: varying quantity, quality and types of known victim audio, verification and identification systems, white- and black-box models and both over-the-wire and over-the-air access. We test the audio transformation on two different proprietary models: (i) the Azure Speaker Recognition API and (ii) the Siri voice activation of an Apple iPhone, showing that individuals can easily be impersonated by obtaining as little as one minute of their audio, even when such audio is recorded in noisy conditions. With attempts from only three source voices, our attack achieves success rates of over 40% in the weakest assumption scenario against the Azure Verification API and rates of over 80% in all scenarios against Siri.
A trajectory planning model of tiltrotor considering multi-phase and multi-mode flight

A trajectory planning model of tiltrotor with multi-phase and multi-mode flight is proposed in this paper. The model is developed to obtain the trajectory of tiltrotor with consideration of flight mission and environment. In the established model, the flight mission from take-off to landing is composed of several phases which are related to the flight modes. On the basis of the flight phases and the flight modes, the trajectory planning model of tiltrotor is described from three aspects: i.e. tiltrotor dynamics including motion equations and maneuverability, flight mission requirements, and flight environment including different no-fly zones. Then, particle swarm optimization algorithm is applied to generate the trajectory of tiltrotor online. The strategy of receding horizon optimization is adopted, and the control inputs in the next few seconds are optimized by particle swarm optimization algorithm. Flight mission simulations with different situations are carried out to verify the rationality and validity of the proposed trajectory planning model. Simulation results demonstrate that the tiltrotor flying with multi-mode can reach the target in three cases and can avoid both static and dynamic obstacles.
Sparse Wavelet Decomposition and Filter Banks with CNN Deep Learning for Speech Recognition

In this paper, the speech recognition algorithms using CNN deep learning based on the sparse discrete wavelet decomposition (SDWD) and bandpass filter banks (BPFB) are proposed. The proposed algorithms consist of three stages. First, speech signal is decomposed into sub-band signals according to the Mel filter bank frequency specification using the SDWD or BPFB. The power values from sub-bands form a feature vector for the speech frame. Cascading feature vectors for consecutive speech frames constructs a two-dimension feature image. Secondly, each obtained feature image is subject to flipping operations in order to reduce edge effect when using the standard CNN. Finally, the CNN deep learning is adopted for training and recognition. The experimental results demonstrate that our proposed SDWD-CNN and BPFB-CNN outperforms the support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF) algorithms.
Road Garbage Cleaning Device Based on ZigBee Gateway and Image Recognition

The existing road garbage automatic cleaning device has problems such as low cleaning efficiency, difficulty in management, low accuracy of information transmission and garbage identification, and cannot meet actual needs well. A road garbage cleaning device based on ZigBee gateway and image recognition is designed, which consist of power module, camera pan/tilt module, robot arm module, ZigBee gateway module, GPS module, MCU control module and vehicle model module. The road debris is identified and analyzed by the camera pan/tilt module together with the RBF neural network algorithm, and then the device can automatically go to the target location and clean up garbage by robotic arm; The networking characteristics of the ZigBee gateway module are used to collect information such as positioning and moving speed of each device node in real time. In addition, the APP software or server can view road image information and device information, analyze relevant data, and provide convenience for real-time monitoring and management of road garbage cleaning work.
The random component-wise power method

This paper considers a random component-wise variant of the unnormalized power method, which is similar to the regular power iteration except that only a random subset of indices is updated in each iteration. For the case of normal matrices, it was previously shown that random component-wise updates converge in the mean-squared sense to an eigenvector of eigenvalue 1 of the underlying matrix even in the case of the matrix having spectral radius larger than unity. In addition to the enlarged convergence regions, this study shows that the eigenvalue gap does not directly affect the convergence rate of the randomized updates unlike the regular power method. In particular, it is shown that the rate of convergence is affected by the phase of the eigenvalues in the case of random component-wise updates, and the randomized updates favor negative eigenvalues over positive ones. As an application, this study considers a reformulation of the component-wise updates revealing a randomized algorithm that is proven to converge to the dominant left and right singular vectors of a normalized data matrix. The algorithm is also extended to handle large-scale distributed data when computing an arbitrary rank approximation of an arbitrary data matrix. Numerical simulations verify the convergence of the proposed algorithms under different parameter settings.
Conditional GANs for Image Captioning with Sentiments

The area of automatic image captioning has witnessed much progress recently. However, generating captions with sentiment, which is a common dimension in human generated captions, still remains a challenge. This work presents a generative approach that combines sentiment (positive/negative) and variation for caption generation. The presented approach consists of a Generative Adversarial Network which takes as input, an image and a binary vector indicating the sentiment of the caption to be generated. We evaluate our model quantitatively on the state-of-the-art image caption dataset and qualitatively using a crowdsourcing platform. Our results, along with human evaluation prove that we competitively succeed in the task of creating variations and sentiment in image captions.
Object detection and tracking basics: Student education

Computer vision is a rapidly developing field of human knowledge. Due to the fullness of images information visual sensors, unlike other types of sensors, theoretically can cover the entire amount of information necessary for autonomous driving, registration of traffic violations, in various medical tasks and so on. The requirements for computer vision specialists are growing every day. At the same time, it is advisable to train specialists in related fields the basics of computer vision. This paper provides an overview of the main points that should be taken into account during the education process in the field of object detection and tracking using computer vision algorithms.
New quantum key agreement protocol with five-qubit Brown states

We propose a new two-party quantum key agreement (QKA) protocol using five-qubit Brown states. One-way quantum transmission can be realized by merging Brown states and decoy photons randomly. The security of this protocol is shown to resist the outsider attack and participant attack over the ideal channel. Some methods are also proposed to ensure its security in noisy and lossy quantum channel. Finally, we generalize it and propose a multi-party QKA protocol based on Brown states.
Deep Integrated Biomarkers of Aging

Recent advances in deep learning (DL) and other machine learning methods (ML) and the availability of large population data sets annotated with age and other features led to the development of the many predictors of chronological ageChronological age (CA) frequently referred to as the deep agingAging clocks (DACs). Many of these aging clocks were pioneered by Insilico Medicine and are available for testing online via www.young.ai , www.aging.ai , and other resources. Several of the DACs demonstrate biological relevance and can be used for a variety of applications ranging from aging and disease target identification, identifying the age-related features implicated in diseases, the inference of causal graphs, analyze the population-specificity of the data type and analyze the mechanism of drug response and many others. The generative adversarial networks (GANs) can be used to synthesize new data of patients of specified ages in large volume providing for novel target identification techniques, as well as for anonymization and patient privacy methods. In this chapter we provide a brief overview of the DACs, their advantages, and disadvantages as well as the commentary on the challenges in this emerging field.
Personas and Persons - An Empirical Study on Stereotyping of Personas

Personas are considered a key method to induce empathy for users. However, they are also said to trigger stereotyping, e.g. by using photographs that convey information about gender, ethnicity, or age of the persona. To investigate this assumption, we conducted an eye-tracking study with 93 students from degree programs in computer science. The participants received an - otherwise identical - persona description with different visualizations in four experimental conditions: a) the photo of a dark-skinned woman, b) the photo of a light-skinned woman, c) a drawing (sketch) or d) no visualization. We examined the conditions with regard to their influence on perception in terms of the "stereotype content model" and other attributes of the persona. The results show that the type of visual representation had certain influence on the perception of the persona and could somewhat reduce stereotyping. Gender and cultural background of the participants had significant influences. We discuss the results against the backdrop of in-group/out-group phenomena and stereotypes.
Determinants and Prospects for the Legal Harmonization of the Intra-BRICS Trade Turnover in the Digital Form

The article examines the features of civil legal regulation of digital assets and regulation of entering into foreign trade transactions in the form of smart contracts of business entities from the BRICS countries. The priority of the Russian Federation participation in BRICS is determined by dynamically developing economic ties, primarily in the spheres of production, energy, information and communication technologies, and cyber security provision. The determinants of digitalization of the integration union economic space are analyzed, on the basis of which the existence of the root causes (financial, economic) and derived causes (legal, political, and social) of the process is argued. The issue of increasing the interest of business entities to technological innovations, as a way to verify counterparties at the pre-contractual stage of concluding a foreign trade transaction and ensuring the fulfillment of obligations independently of the parties, is studied. It is proved that the use of the benefits of smart contracts is currently hampered by the lack of appropriate regulation, both at the national and international legal levels. The existing acts concluded by the BRICS member countries are, in their essence, of programmatic character and do not contain specific material norms. Based on the comparative legal analysis of the legal systems of the states belonging to the integration union, the reasons for the lack of unified legislation are disclosed involving radically different legal traditions, as well as in the desire of each individual state to preserve national sovereignty and independently determine foreign policy in the sphere of trade and investment. The difficulty of creating a single package of material norms on civil law regulation of foreign trade transactions in the form of smart contracts, moreover, is seen in the significant difference in the choice of methods and means of legal regulation by national legislators both within domestic international private law in general and economic and contractual use of blockchain technologies exacerbated by the foreign element, in particular. It is concluded that, taking into consideration the difficulty of implementing unification by creating “uniform” rules, the Model Code of foreign trade transactions performed through smart contracts should be adopted. The said code in a dispositive manner must contain the general beginnings of the procedure for electronic registration of the economic relations’ parties, the order of expression of will and confirming performance of obligations, including the use of chipization of goods crossing the border; as well as the basic applicable connecting factors of choosing the law and the court in whose jurisdiction are cases in this category.
Abnormal Behavior Detection Based on Optical Flow Trajectory of Human Joint Points

Detection of abnormal behavior of pedestrians in public places is always one of the key issues for public security. Video surveillance is an effective approach to address the issue. Considering the characteristics of multi-person poses, we propose an effective and practical method to detect the abnormality based on the optical flow trajectory of joint points for each human body. There are 4 basic steps in the proposed method: firstly, estimate the posture of each individual in the crowd to determine the corresponding joint points; secondly, compute the optical flow field of the joint points for each person; thirdly, making use of the trajectory constraints for the computed optical flow field, extract feature vectors after noise removal and vector trajectories synthesis of the joint points of each pedestrian; finally, use SVM (Support Vector Machine) to determine abnormal behavior. Experimental results show this method can effectively detect the abnormal behavior of pedestrians in the crowd.