Reviewerly/exHarmony huggingface dataset:
Team wisdom in software development projects and its impact on project performance

While the concept of wisdom, which refers to how people make right use of their knowledge through their practical actions, judgments, and ethical decisions, in general attracts researcher interest in a variety of disciplines, such as philosophy, psychology and management studies, little is known about how wisdom is conceptualized and then operationalized in the software development project team context. Based on the frameworks for philosophical, group and organizational wisdom, this paper identifies software development project team wisdom as a process for how team members best use the stock and flow of their knowledge through collective judgment, virtue-ethics, emotions/feelings, and effective decision-making during their project-related efforts. Adapting the efforts and functional similarities of both group and organizational wisdom practices, this effort determines that wisdom-related mechanisms (e.g., team diversity, networking with other teams and people, and their past experiences), joint epistemic actions (e.g., team reasoning, intuition, and aesthetic capacity), and team virtue and prudence become the different faces of the software development team wisdom process. We then propose how these different faces interrelate and how they also relate to project process effectiveness, such as team learning and speed-to-users, both of which have been rarely addressed empirically in the context of software development project teamwork. By examining 210 in-house software development project teams in a field study and using structural equation modeling analysis, our results empirically show the following: (a) software development wisdom-related mechanisms positively relate to software development team prudence and virtue and their joint epistemic actions, (b) software development team prudence and virtue are positively associated with software development team joint epistemic actions, and further (d) software development team joint epistemic actions are positively associated with software development project process effectiveness. We conclude by discussing our findings as they relate to the wisdom framework of software development project teams and suggest the key managerial implications for different types of software development projects.
Zero-Shot Task Transfer

In this work, we present a novel meta-learning algorithm that regresses model parameters for novel tasks for which no ground truth is available (zero-shot tasks). In order to adapt to novel zero-shot tasks, our meta-learner learns from the model parameters of known tasks (with ground truth) and the correlation of known tasks to zero-shot tasks. Such intuition finds its foothold in cognitive science, where a subject (human baby) can adapt to a novel concept (depth understanding) by correlating it with old concepts (hand movement or self-motion), without receiving an explicit supervision. We evaluated our model on the Taskonomy dataset, with four tasks as zero-shot: surface normal, room layout, depth and camera pose estimation. These tasks were chosen based on the data acquisition complexity and the complexity associated with the learning process using a deep network. Our proposed methodolgy outperforms state-of-the-art models (which use ground truth) on each of our zero-shot tasks, showing promise on zero-shot task transfer. We also conducted extensive experiments to study the various choices of our methodology, as well as showed how the proposed method can also be used in transfer learning. To the best of our knowledge, this is the first such effort on zero-shot learning in the task space.
Privacy-Preserving Distributed Machine Learning via Local Randomization and ADMM Perturbation

With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks. However, privacy concerns have to be given priority in DML, since training data may contain sensitive information of users. In this paper, we propose a privacy-preserving ADMM-based DML framework with two novel features: First, we remove the assumption commonly made in the literature that the users trust the server collecting their data. Second, the framework provides heterogeneous privacy for users depending on data's sensitive levels and servers' trust degrees. The challenging issue is to keep the accumulation of privacy losses over ADMM iterations minimal. In the proposed framework, a local randomization approach, which is differentially private, is adopted to provide users with self-controlled privacy guarantee for the most sensitive information. Further, the ADMM algorithm is perturbed through a combined noise-adding method, which simultaneously preserves privacy for users' less sensitive information and strengthens the privacy protection of the most sensitive information. We provide detailed analyses on the performance of the trained model according to its generalization error. Finally, we conduct extensive experiments using real-world datasets to validate the theoretical results and evaluate the classification performance of the proposed framework.
A Blockchain Proxy for Lightweight IoT Devices

IoT devices are quickly becoming a critical source of information about the physical world considered in business processes. Blockchains are a promising platform for such processes if they involve multiple parties with no shared, commonly trusted IT infrastructure. Transacting with a blockchain, however, requires software whose footprint overwhelms many lightweight IoT devices. In this paper we introduce the concept of a blockchain proxy to which an IoT device can offload a large part of this software footprint. The proxy only requires a slim proxy SDK on the device that holds a regular blockchain identity with its own private key, retaining full control of the transactions in the device. We discuss security implications and present cold-chain monitoring as a use case. Preliminary results show significant savings in CPU time and communication bandwidth for the IoT device.
Autonomous Systems – An Architectural Characterization

The concept of autonomy is key to the IoT vision promising increasing integration of smart services and systems minimizing human intervention. This vision challenges our capability to build complex open trustworthy autonomous systems. We lack a rigorous common semantic framework for autonomous systems. It is remarkable that the debate about autonomous vehicles focuses almost exclusively on AI and learning techniques while it ignores many other equally important autonomous system design issues. Autonomous systems involve agents and objects coordinated in some common environment so that their collective behavior meets a set of global goals. We propose a general computational model combining a system architecture model and an agent model. The architecture model allows expression of dynamic reconfigurable multi-mode coordination between components. The agent model consists of five interacting modules implementing each one a characteristic function: Perception, Reflection, Goal management, Planning and Self-adaptation. It determines a concept of autonomic complexity accounting for the specific difficulty to build autonomous systems. We emphasize that the main characteristic of autonomous systems is their ability to handle knowledge and adaptively respond to environment changes. We advocate that autonomy should be associated with functionality and not with specific techniques. Machine learning is essential for autonomy although it can meet only a small portion of the needs implied by autonomous system design. We conclude that autonomy is a kind of broad intelligence. Building trustworthy and optimal autonomous systems goes far beyond the AI challenge.
Cyber Threat Information Sharing

The literature on cyber security information sharing enumerates an extensive list of potential benefits for organisations in both the public and private sectors. However, despite the potential benefits, successful cyber security information sharing has been difficult to achieve. We report upon a study that sought to measure the extent to which the benefits and barriers suggested by the cyber security information sharing literature are reflected in the attitudes of practising security managers and analysts.
Crypto Hash Algorithm-Based Blockchain Technology for Managing Decentralized Ledger Database in Oil and Gas Industry

This research work proposes a method for the securing and monitoring of petroleum product distribution records in a decentralized ledger database using blockchain technology. The aim of using this technique is to secure the transaction of distributed ledgers in a database and to protect records from tampering, fraudulent activity, and corruption by the chain participants. The blockchain technology approach offers an efficient security measure and novel advantages, such as in the transaction existence and distribution ledger management between the depot, transporter, and retailing filling station. Others advantages are transparency, immunity to fraud, insusceptibility to tampering, and maintaining record order. The technique adopted for this secure distributed ledger database is crypto hash algorithm-1 (SHA-1)-based public permissioned blockchain and telematics, while this telematics approach is an embedded system integrated into an in-vehicle model for remote tracking of geolocation (using Global Positioning System (GPS)), monitoring, and far-off data acquisition in a real-time. The scope of the data in the secure distributed ledger database (using blockchain) developed are identification (ID) of the tanker operator, Depot name, Source station ID, Destination station ID, Petroleum product volume, Transporter ID, and Geographic automobiles location. This system proved to be efficient, secure, and easy to maintain as it does not permit any individual for records tampering, but supports agreement of ~75% of participants in the chain to make changes.
An Accurate Nuclei Segmentation Algorithm in Pathological Image Based on Deep Semantic Network

Cell (nuclei) segmentation is the basic and key step of pathological image analysis.However, robust and accurate cell (nuclei) segmentation is a difficult problem due to the enormous variability of staining, cell sizes, morphologies and cell adhesion or overlapping.In this paper, we extend U-Net with atrous depthwise separable convolution (AS-UNet) for cell (nuclei) segmentation.AS-UNet consists of three parts: encoder module, decoder module and atrous convolution module.The encoder module obtains the highlevel semantic information of the cell image layer by layer, while the decoder module gradually recovers the spatial information.The atrous convolution module is composed of cascade and parallel atrous convolution operations.It can extract and combine multi-scale features so that the model can get strong perception ability for both small and large cells.At the same time, the atrous convolution can significantly increase the receptive field of the network model without hurting the segmentation performance or increasing the computational cost.During the training period, Log-Dice loss and Focal loss are combined, while Adam optimization method is employed to optimize the network.In order to increase the penalty for the smaller prediction result of the Dice coefficient, which is carried out by logarithmic operation and the negative value is taken as the Log-Dice loss.The above optimization is beneficial for the convergence speed.Additionally, some data augmentation techniques are applied to increase online data, which contribute to improving the robustness of the model.Compared with several state-of-the-art semantic segmentation algorithms, our method achieves the promising performance on two latest released pathological image datasets.
Why aren’t regular expressions a lingua franca? an empirical study on the re-use and portability of regular expressions

This paper explores the extent to which regular expressions (regexes) are portable across programming languages. Many languages offer similar regex syntaxes, and it would be natural to assume that regexes can be ported across language boundaries. But can regexes be copy/pasted across language boundaries while retaining their semantic and performance characteristics?
Noisy Private Information Retrieval: On Separability of Channel Coding and Information Retrieval

We consider the problem of noisy private information retrieval (NPIR) from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> non-communicating databases, each storing the same set of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> messages. In this model, the answer strings are not returned through noiseless bit pipes, but rather through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">noisy</i> memoryless channels. We aim at characterizing the PIR capacity for this model as a function of the statistical information measures of the noisy channels such as entropy and mutual information. We derive a general upper bound for the retrieval rate in the form of a max-min optimization. We use the achievable schemes for the PIR problem under asymmetric traffic constraints and random coding arguments to derive a general lower bound for the retrieval rate. The upper and lower bounds match for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M=2$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M=3$ </tex-math></inline-formula> , for any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> , and any noisy channel. The lower and upper bounds show a separation between channel coding and retrieval scheme except for adapting the traffic ratio from the databases. We refer to this as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">almost separation</i> . Next, we consider the private information retrieval problem from multiple access channels (MAC-PIR). In MAC-PIR, the database responses reach the user through a multiple access channel (MAC) that mixes the responses together in a stochastic way. We show that for the additive MAC and the conjunction/disjunction MAC, channel coding and retrieval scheme are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inseparable</i> unlike in NPIR. We show that the retrieval scheme depends on the properties of the MAC, in particular on the linearity aspect. For both cases, we provide schemes that achieve the full capacity without any loss due to the privacy constraint, which implies that the user can exploit the nature of the channel to improve privacy. Finally, we show that the full unconstrained capacity is not always attainable by determining the capacity of the selection channel.
Real-Time Panoramic Depth Maps from Omni-directional Stereo Images for 6 DoF Videos in Virtual Reality

In this paper we present an approach for 6 DoF panoramic videos from omni-directional stereo (ODS) images using convolutional neural networks (CNNs). More specifically, we use CNNs to generate panoramic depth maps from ODS images in real-time. These depth maps would then allow for re-projection of panoramic images thus providing 6 DoF to a viewer in virtual reality (VR). As the boundaries of a panoramic image must touch in order to envelope a viewer, we introduce a border weighted loss function as well as new error metrics specifically tailored for panoramic images. We show experimentally that training with our border weighted loss function improves performance by benchmarking a baseline skip-connected encoder-decoder style network as well as other state-of-the-art methods in depth map estimation from mono and stereo images. Finally, a practical application for VR using real world data is also demonstrated.
Evolutionary Optimization-based Mission Planning for UAS Traffic Management (UTM)

In this work, we propose a route planning scheme for large scale Unmanned Aircraft System (UAS) or multiple-drone operations in complex urban air space. The route planning system consists of a path finding algorithm and a scheduling system for safe and efficient uses of airspace. The work flow of the system is: first, generate shortest paths between origination and destination points via heuristic search and second, schedule the submitted flights to avoid possible conflicts. The scheduling process is performed by Evolutionary Algorithm (EA). Such a system would postpone or delay flight requests that may collide with previous flights and reject the conflicted flights. We also propose a fitness function for the EA to minimize both the total delay of the flight requests and the possibility of UAS collision. To demonstrate the feasibility of the proposed route planning models, preliminary simulations in a local town in Singapore are presented and discussed.
Maximal multi-layer specification synthesis

There has been a significant interest in applying programming-by-example to automate repetitive and tedious tasks. However, due to the incomplete nature of input-output examples, a synthesizer may generate programs that pass the examples but do not match the user intent. In this paper, we propose MARS, a novel synthesis framework that takes as input a multi-layer specification composed by input-output examples, textual description, and partial code snippets that capture the user intent. To accurately capture the user intent from the noisy and ambiguous description, we propose a hybrid model that combines the power of an LSTM-based sequence-to-sequence model with the apriori algorithm for mining association rules through unsupervised learning. We reduce the problem of solving a multi-layer specification synthesis to a Max-SMT problem, where hard constraints encode well-typed concrete programs and soft constraints encode the user intent learned by the hybrid model. We instantiate our hybrid model to the data wrangling domain and compare its performance against Morpheus, a state-of-the-art synthesizer for data wrangling tasks. Our experiments demonstrate that our approach outperforms MORPHEUS in terms of running time and solved benchmarks. For challenging benchmarks, our approach can suggest candidates with rankings that are an order of magnitude better than MORPHEUS which leads to running times that are 15x faster than MORPHEUS.
Exploring the gap between the student expectations and the reality of teamwork in undergraduate software engineering group projects

Software engineering group projects aim to provide a nurturing environment for learning about teamwork in software engineering. Since social and teamwork issues have been consistently identified as serious problems in such projects, we aim to better understand the breakdown between the expectations teams have at the start of a group project and their experiences at the end of the project. In this paper, we investigate how 35 teams of undergraduate students approach software engineering group project courses, and how their previous experience with collaborative software development matches their expectations for group work. We then analyse the retrospective documents delivered by the same teams at the end of a 27-week software engineering group project course, mirroring the expectations at the start of the project with the realities described by the end of it.
Two Projection Neural Networks With Reduced Model Complexity for Nonlinear Programming

Recent reports show that projection neural networks with a low-dimensional state space can enhance computation speed obviously. This paper proposes two projection neural networks with reduced model dimension and complexity (RDPNNs) for solving nonlinear programming (NP) problems. Compared with existing projection neural networks for solving NP, the proposed two RDPNNs have a low-dimensional state space and low model complexity. Under the condition that the Hessian matrix of the associated Lagrangian function is positive semi-definite and positive definite at each Karush-Kuhn-Tucker point, the proposed two RDPNNs are proven to be globally stable in the sense of Lyapunov and converge globally to a point satisfying the reduced optimality condition of NP. Therefore, the proposed two RDPNNs are theoretically guaranteed to solve convex NP problems and a class of nonconvex NP problems. Computed results show that the proposed two RDPNNs have a faster computation speed than the existing projection neural networks for solving NP problems.
Multi-View Image Classification With Visual, Semantic and View Consistency

Multi-view visual classification methods have been widely applied to use discriminative information of different views. This strategy has been proven very effective by many researchers. On the one hand, images are often treated independently without fully considering their visual and semantic correlations. On the other hand, view consistency is often ignored. To solve these problems, in this paper, we propose a novel multi-view image classification method with visual, semantic and view consistency (VSVC). For each image, we linearly combine multi-view information for image classification. The combination parameters are determined by considering both the classification loss and the visual, semantic and view consistency. Visual consistency is imposed by ensuring that visually similar images of the same view are predicted to have similar values. For semantic consistency, we impose the locality constraint that nearby images should be predicted to have the same class by multiview combination. View consistency is also used to ensure that similar images have consistent multi-view combination parameters. An alternative optimization strategy is used to learn the combination parameters. To evaluate the effectiveness of VSVC, we perform image classification experiments on several public datasets. The experimental results on these datasets show the effectiveness of the proposed VSVC method.
Deep network for human action recognition using Weber motion

Effective motion estimation is one of the prime steps for any human action recognition (HAR) algorithm. Optical flow (OF) and motion history image (MHI) are two well-known methods for motion estimation in videos. OF has several advantages over MHI. But the major drawback with OF is that it is computationally very expensive as compared to the MHI. Therefore, in this paper, a new motion estimation technique named as Weber Motion History Image (WMHI) is proposed. Here, an extremely fast algorithm is proposed for HAR using WMHI, pose information, and convolutional neural network (CNN). In spite of being fast and less space consuming, the algorithm outperforms the existing pose based CNN results on five benchmark datasets namely JHMDB [1], sub-JHMDB [1], MPII [2] and HMDB51 [3] and UCF101 [4]. The work mainly focuses on a new efficient algorithm which can be implemented for real-time HAR in videos. For real-time implementation, the two basic criteria on which an algorithm can be analyzed are space and time complexity. The proposed algorithm is faster as compared to the existing OF based HAR systems. In terms of space complexity, the feature size of the proposed algorithm is almost 50% of the existing OF based algorithm. The recognition results still outperform the existing result by a significant margin.