Reviewerly/exHarmony huggingface dataset:
A survey on software coupling relations and tools

Coupling relations reflect the dependencies between software entities and can be used to assess the quality of a program. For this reason, a vast amount of them has been developed, together with tools to compute their related metrics. However, this makes the coupling measures suitable for a given application challenging to find. The first objective of this work is to provide a classification of the different kinds of coupling relations, together with the metrics to measure them. The second consists in presenting an overview of the tools proposed until now by the software engineering academic community to extract these metrics. This work constitutes a systematic literature review in software engineering. To retrieve the referenced publications, publicly available scientific research databases were used. These sources were queried using keywords inherent to software coupling. We included publications from the period 2002 to 2017 and highly cited earlier publications. A snowballing technique was used to retrieve further related material. Four groups of coupling relations were found: structural, dynamic, semantic and logical. A fifth set of coupling relations includes approaches too recent to be considered an independent group and measures developed for specific environments. The investigation also retrieved tools that extract the metrics belonging to each coupling group. This study shows the directions followed by the research on software coupling: e.g., developing metrics for specific environments. Concerning the metric tools, three trends have emerged in recent years: use of visualization techniques, extensibility and scalability. Finally, some coupling metrics applications were presented (e.g., code smell detection), indicating possible future research directions. Public preprint [https://doi.org/10.5281/zenodo.2002001].
Saliency Detection via Multi-Scale Global Cues

The saliency detection technologies are very useful to analyze and extract important information from given multimedia data, and have already been extensively used in many multimedia applications. Past studies have revealed that utilizing the global cues is effective in saliency detection. Nevertheless, most of prior works mainly considered the single-scale segmentation when the global cues are employed. In this paper, we attempt to incorporate the multi-scale global cues for saliency detection problem. Achieving this proposal is interesting and also challenging (e.g., How to obtain appropriate foreground and background seeds effectively? How to merge rough saliency results into the final saliency map efficiently?). To alleviate the challenges, we present a three-phase solution that integrates several targeted strategies, first, a self-adaptive strategy for obtaining appropriate filter parameters; second, a cross-validation scheme for selecting appropriate background and foreground seeds; and third, a weight-based approach for merging the rough saliency maps. Our solution is easy to understand and implement, but without loss of effectiveness. Extensive experimental results based on benchmark datasets demonstrate the feasibility and competitiveness of our proposed solution.
High purity single photons entangled with an atomic qubit

Trapped atomic ions are an ideal candidate for quantum network nodes, with long-lived identical qubit memories that can be locally entangled through their Coulomb interaction and remotely entangled through photonic channels. The integrity of this photonic interface is generally reliant on purity of single photons produced by the quantum memory. Here we demonstrate a single-photon source for quantum networking based on a trapped $^{138}\mbox{Ba}^+$ ion with a single photon purity of $ g^{2}(0)=(8.1\pm2.3)\times 10^{-5} $ without background subtraction. We further optimize the tradeoff between the photonic generation rate and the memory-photon entanglement fidelity for the case of polarization photonic qubits by tailoring the spatial mode of the collected light.
Convolutional network architectures for super-resolution/sub-pixel mapping of drone-derived images

Spatial resolution enhancement is a pre-requisite for integrating unmanned aerial vehicle (UAV) datasets with the data from other sources. However, the mobility of UAV platforms, along with radiometric and atmospheric distortions, makes the task difficult. In this paper, various convolutional neural network (CNN) architectures are explored for resolving the issues related to sub-pixel classification and super-resolution of drone-derived datasets. The main contributions of this work are: 1) network-inversion based architectures for super-resolution and sub-pixel mapping of drone-derived images taking into account their spectral-spatial characteristics and the distortions prevalent in them 2) a feature-guided transformation for regularizing the inversion problem 3) loss functions for improving the spectral fidelity and inter-label compatibility of coarser to finer-scale mapping 4) use of multi-size kernel units for avoiding over-fitting. The proposed approach is the first of its kind in using neural network inversion for super-resolution and sub-pixel mapping. Experiments indicate that the proposed super-resolution approach gives better results in comparison with the sparse-code based approaches which generally result in corrupted dictionaries and sparse codes for multispectral aerial images. Also, the proposed use of neural network inversion, for projecting spatial affinities to sub-pixel maps, facilitates the consideration of coarser-scale texture and color information in modeling the finer-scale spatial-correlation. The simultaneous consideration of spectral bands, as proposed in this study, gives better super-resolution results when compared to the individual band enhancements. The proposed use of different data-augmentation strategies, for emulating the distortions, improves the generalization capability of the framework. Sensitivity of the proposed super-resolution and sub-pixel mapping frameworks with regard to the network parameters is thoroughly analyzed. The experiments over various standard datasets as well as those collected from known locations indicate that the proposed frameworks perform better when compared to the prominent published approaches.
Developing Effective and Resilient Human-Agent Teamwork Using Team Design Patterns

Human-agent teams exhibit emergent behavior at the team level, as a result of interactions between individuals within the team. This begs the question how to design artificial team members (agents) as adequate team players that contribute to the team processes advancing team performance, resilience, and learning. This paper proposes the development of a library of team design patterns as a way to make dynamic team behavior at the team and individual level more explicit. Team design patterns serve a dual purpose: (1) in the system development phase, designers can identify desirable team patterns for the creation of artificial team members. (2) During the operational phase, team design patterns can be used by artificial team members to drive and stimulate team development and to adaptively mitigate problems that may arise. We describe a pattern language for specifying team design patterns, discuss their use, and illustrate the concept using representative human-agent teamwork applications.
Instance Segmentation Enabled Hybrid Data Association and Discriminative Hashing for Online Multi-Object Tracking

Online multi-object tracking remains a difficult problem in complex scenes because of inaccurate detections, frequent occlusions by clutter or other objects, similar appearances of different objects, and other factors. In this paper, we propose a hybrid data association strategy combined with instance segmentation and online feature learning to handle these difficulties effectively. First, we utilize an instance segmentation algorithm to separate each object from other objects and backgrounds in a pixel-to-pixel manner, which will help resolve typical difficulties in multi-object tracking, such as ID-switches and track drifting. Moreover, we propose a local-to-global hybrid data association strategy to take advantage of the superiorities of both online and batch data association methods. The local data association between observations in consecutive frames reduces the computational complexity, hence ensuring the efficiency of online tracking. The global data association complements the local data association by integrating multiple video frames, thus alleviating the fragmented tracklets. Last, to improve the appearance discriminability and make it more robust in dealing with appearance variations during tracking, a lightweight semantic-preserving hashing algorithm has been proposed to learn compact hash codes online. Experiments with the MOT17 Challenge dataset demonstrate the superior performance of the proposed approach over other state-of-the-art batch and online tracking methods.
Semiquantum key distribution with high quantum noise tolerance

Semi-quantum key distribution protocols are designed to allow two parties to establish a shared secret key, secure against an all-powerful adversary, even when one of the users is restricted to measuring and preparing quantum states in one single basis. While interesting from a theoretical standpoint, these protocols have the disadvantage that a two-way quantum communication channel is necessary which generally limits their theoretical efficiency and noise tolerance. In this paper, we construct a new semi-quantum key distribution (SQKD) protocol which actually takes advantage of this necessary two-way channel, and, after performing an information theoretic security analysis against collective attacks, we show it is able to tolerate a channel noise level higher than any prior SQKD protocol to-date. We also compare the noise tolerance of our protocol to other two-way fully quantum protocols, along with BB84 with Classical Advantage Distillation (CAD). We also comment on some practical issues involving semi-quantum key distribution (in particular, concerning the potential complexity in physical implementation of our protocol as compared with other standard QKD protocols). Finally, we develop techniques that can be applied to the security analysis of other (S)QKD protocols reliant on a two-way quantum communication channel.
Blockchain-Based Security and Privacy in Smart Cities

It is evident that the era of the Internet of Things (IoT) is already knocking on our door [43]. Cities are growing by the day, and current integrated information and communication technologies (ICT) are invited to adapt to the new reality. The proliferation of connected nodes, combined with the urgency of new designs of products in order to accommodate this new era, are raising major security issues that the ICT community has been asked to examine. Is the existing technology able to achieve a secure environment within the vastly developing smart cities? There are many factors that ought to be taken into consideration, apart from solely securing the smart cities. Specifically, the newly developed technologies need to be able to reduce consumed resources, as well as increase the quality of life of the citizens, without jeopardizing their privacy. The use of blockchain-based systems attempts to address these emerging issues. In particular, the blockchain model presents an opportunity to end the current practice of storing information on third-party ledgers, which requires vast data security resources to secure their perimeter [6]. This chapter aims, primarily, to address the security issues that unfold during the transition from the conventional way of living to the interconnected world of smart cities. Moreover, we will take a thorough look at the blockchain model, and explore why this may be considered the answer to the security and privacy issues that arise from the new era. In particular, we will explore the interaction of blockchain with smart contracts and their application in e-governance. This seems to be gaining ground towards the more general concept of e-democracy. Well-known models of e-residency [36] will be used as a point of reference in order to make a more thorough investigation of its introduction and implementation in the world of smart contracts. This study will make suggestions for improvement and additions of new scripts that might be able to assist the further improvement of this challenging and overly ambitious venture.
A novel fused coupled chaotic map based confidential data embedding-then-encryption of electrocardiogram signal

In telecardiology applications, research has been underway to protect the patient's confidential information from unauthorized access using Electrocardiogram (ECG) steganography or encryption approaches. A novel Fused Coupled Chaotic Map (FCCM) structure based integrated embedding-then-encryption approach is proposed for patient's confidential data embedding in ECG and its subsequent encryption. The proposed ECG embedding-then-encryption algorithm consists of four phases: confidential data ciphering, LSB embedding, substitution, and permutation. Four FCCMs under different initial conditions and control parameters undergoes level shifting or sorting based quantization. The updation of control parameters made to be ECG content aware of security enhancement. The generated {FCCMi,: i ∈ (1–4)} are applied to (i) cipher confidential data, (ii) craft the embedding locations to hide segmented-ciphered confidential data, (iii) substitution and (iv) permutation processes for data embedding-then-encryption of ECG. The performance of the proposed method was evaluated over MIT-BIH Arrhythmia and self-recorded ECG (lead-II) database. The test suites NIST-800 and 0-1 are employed to check randomness of chaotic sequences. The PRD, PRDN, PRD1024, RMS, SNR, PSNR, WWPRD, WEDD, and BERs were 0.04, 1.05, 0.75, 5.55 × 10−4, 41.21, 70.17, 3.02 × 10−2, 5.91 × 10−3, and 0.00 for MIT-BIH Arrhythmia ECG database (Lead-II) at payload 2.4Kb. Performance measures for Self-recorded dataset are 0.04, 2.62, 2.62, 5.49 × 10−4, 32.16, 68.72, 9.36 × 10−2, 1.93 × 10−2, and 0.00 respectively. Experimental results, security analysis and comparison with existing state-of-art methods prove the effectiveness of the proposed approach for assured patient's confidential data security. Furthermore, the efficiency of the proposed approach has also been evaluated on compressed ECG.
Quantum walk search on the complete bipartite graph

The coined quantum walk is a discretization of the Dirac equation of relativistic quantum mechanics, and it is the basis of many quantum algorithms. We investigate how it searches the complete bipartite graph of $N$ vertices for one of $k$ marked vertices with different initial states. We prove an intriguing dependence on the number of marked and unmarked vertices in each partite set. For example, when the graph is irregular and the initial state is the typical uniform superposition over the vertices, then the success probability can vary greatly from one time step to the next, even alternating between 0 and 1, so the precise time at which measurement occurs is crucial. When the initial state is a uniform superposition over the edges, however, the success probability evolves smoothly. As another example, if the complete bipartite graph is regular, then the two initial states are equivalent. Then if two marked vertices are in the same partite set, the success probability reaches $1/2$, but if they are in different partite sets, it instead reaches 1. This differs from the complete graph, which is the quantum walk formulation of Grover's algorithm, where the success probability with two marked vertices is $8/9$. This reveals a contrast to the continuous-time quantum walk, whose evolution is governed by Schr\"odinger's equation, which asymptotically searches the regular complete bipartite graph with any arrangement of marked vertices in the same manner as the complete graph.
Piecewise regression analysis through information criteria using mathematical programming

Regression is a predictive analysis tool that examines the relationship between independent and dependent variables. The goal of this analysis is to fit a mathematical function that describes how the value of the response changes when the values of the predictors vary. The simplest form of regression is linear regression which in the case multiple regression, tries to explain the data by simply fitting a hyperplane minimising the absolute error of the fitting. Piecewise regression analysis partitions the data into multiple regions and a regression function is fitted to each one. Such an approach is the OPLRA (Optimal Piecewise Linear Regression Analysis) model (Yang, Liu, Tsoka, & Papage, 2016) which is a mathematical programming approach that optimally partitions the data into multiple regions and fits a linear regression functions minimising the Mean Absolute Error between prediction and truth. However, using many regions to describe the data can lead to overfitting and bad results. In this work an extension of the OPLRA model is proposed that deals with the problem of selecting the optimal number of regions as well as overfitting. To achieve this result, information criteria such as the Akaike and the Bayesian are used that reward predictive accuracy and penalise model complexity.
Blind image quality assessment with hierarchy: Degradation from local structure to deep semantics

Though blind image quality assessment (BIQA) is highly desired in perceptual-oriented image processing systems, it is extremely difficult to design a reliable BIQA method. With the help of the prior knowledge, the human visual system (HVS) hierarchically perceives the quality degradation during the visual recognition. Inspired by this, we suggest different levels of distortion generate individual degradations on hierarchical features, and propose to consider the degradations on both low and high level features for quality prediction. By mimicking the orientation selectivity (OS) mechanism in the primary visual cortex, an OS based local structure is designed for low-level visual information representation. At the meantime, the deep residual network, which possesses multiple levels for feature integration, is employed to extract the deep semantics for high-level visual content representation. By fusing the local structure and the deep semantics, a hierarchical feature set is acquired. Next, the correlations between the degradations of image qualities and their corresponding hierarchical feature sets are analyzed, and a novel hierarchical feature degradation (HFD) based BIQA (HFD-BIQA) method is built. Experimental results on the legacy and wild image quality assessment databases demonstrate the prediction accuracy of the proposed HFD-BIQA method, and verify that the HFD-BIQA performs highly consistent with the subjective perception.
Smart Contract-Based Negotiation for Adaptive QoS-Aware Service Composition

Smart contracts (SCs) run on the distributed ledger technology (DLT) platform and can implement agreements between participants without a trusted third party. This paper uses the DLT and SC techniques to build distributed applications composed of existing services. In practice, there are many functionally-equivalent services on the Internet. To beat their competitors, the service providers usually offer flexible QoS and use dynamic pricing strategies. Moreover, the service providers can change at runtime, e.g., they may encounter problems so that their QoS drops suddenly. This makes achieving the optimization goal at runtime (e.g., the maximization of the utility) more difficult. To address this problem, first, this paper proposes an SC-based negotiation framework. The SCs can ensure that the transactions are automatically and reliably performed as agreed upon between the service requesters and providers. The DLTs can provide the reliable data of the requests and responses of the service requesters and providers to the SCs. In addition, the SCs can identify the troubled service providers, and find other service providers to replace them at runtime. Second, this paper proposes a Bayesian Nash equilibrium (BNE) of the service providers. In the BNE, the cost-efficient service providers offer the high QoS the service requester asks for and report their costs truthfully. This BNE enables the selection of the cost-efficient service providers and the achievement of the (near) maximization of the service requesters' utility. This paper implements the proposed negotiation framework on a DLT platform called Hyperledger Fabric. The experiment results demonstrate that the proposed approach outperforms the existing approaches and can adapt to the changes of the service providers.
Privacy Preservation in Smart Cities

The proliferation of smart devices in the Internet of Things has given rise to the Smart City. Despite improvements in the service quality, the Smart City suffers from potential privacy threats. Adversaries can take advantage of Smart Cities and launch various attacks to learn sensitive information about individuals, especially through social links and locations. Motivated by this, we comprehensively survey current attacks and existing protection schemes. In addition, we place special focus on social link privacy and location privacy, which are two primary privacy issues under the scenario of the Smart City. Built upon the survey, we further propose some possible promising future research directions to pave the way for potential privacy protection methods.
SfMLearner++: Learning Monocular Depth &amp; Ego-Motion Using Meaningful Geometric Constraints

Most geometric approaches to monocular Visual Odometry (VO) provide robust pose estimates, but sparse or semi-dense depth estimates. Off late, deep methods have shown good performance in generating dense depths and VO from monocular images by optimizing the photometric consistency between images. Despite being intuitive, a naive photometric loss does not ensure proper pixel correspondences between two views, which is the key factor for accurate depth and relative pose estimations. It is a well known fact that simply minimizing such an error is prone to failures. We propose a method using Epipolar constraints to make the learning more geometrically sound. We use the Essential matrix, obtained using Nistér's Five Point Algorithm, for enforcing meaningful geometric constraints on the loss, rather than using it as labels for training. Our method, although simplistic but more geometrically meaningful, uses lesser number of parameters to give a comparable performance to state-of-the-art methods which use complex losses and large networks showing the effectiveness of using epipolar constraints. Such a geometrically constrained learning method performs successfully even in cases where simply minimizing the photometric error would fail.