Reviewerly/exHarmony huggingface dataset:
A quantum inspired genetic algorithm for multimodal optimization of wind disturbance alleviation flight control system

This paper develops a Quantum-inspired Genetic Algorithm (QGA) to find the sets of optimal parameters for the wind disturbance alleviation Flight Control System (FCS). To search the problem domain more evenly and uniformly, the lattice rule based stratification method is used to create new chromosomes. The chromosomes are coded and updated according to quantum-inspired strategies. A niching method is used to ensure every chromosome can converge to its corresponding local minimum in the optimization process. A parallel archive system is adopted to monitor the chromosomes on-line and save all potential feasible solutions in the optimization process. An adaptive search strategy is used to gradually adjust the search domain of each niche to finally approach the local minima. The solutions found by the QGA are compared with some other Multimodal Optimization (MO) algorithms and are tested on the FCS of the Boeing 747 to demonstrate the effectiveness of the proposed algorithm.
Streaming video database features: What do faculty and students really want?

The adoption of streaming video by academic libraries is widespread, yet little is known about the experiences of faculty and students who use streaming video. This article describes a usability study of three popular streaming video databases. Faculty and student participants completed a series of tasks in each database and answered questions about their use of streaming video. Faculty liked complete and accurate metadata and text descriptions, the use of thumbnail images, accessibility, ease of use, the availability of film segments, the ability to easily share content, and the availability of time-stamped, hyperlinked transcripts. Students liked easy-to-use, intuitive interfaces.
Entanglement Dynamics of Three and Four Level Atomic System under Stark Effect and Kerr-Like Medium

We investigated numerically the dynamics of quantum Fisher information (QFI) and entanglement for three- and four-level atomic systems interacting with a coherent field under the effect of Stark shift and Kerr medium. It was observed that the Stark shift and Kerr-like medium play a prominent role during the time evolution of the quantum systems. The non-linear Kerr medium has a stronger effect on the dynamics of QFI as compared to the quantum entanglement (QE). QFI is heavily suppressed by increasing the value of Kerr parameter. This behavior was found comparable in the cases of three- and four-level atomic systems coupled with a non-linear Kerr medium. However, QFI and quantum entanglement (QE) maintain their periodic nature under atomic motion. On the other hand, the local maximum value of QFI and von Neumann entropy (VNE) decrease gradually under the Stark effect. Moreover, no prominent difference in the behavior of QFI and QE was observed for three- and four-level atoms while increasing the value of Stark parameter. However, three- and four-level atomic systems were found equally prone to the non-linear Kerr medium and Stark effect. Furthermore, three- and four-level atomic systems were found fully prone to the Kerr-like medium and Stark effect.
Asymptotically Efficient Recursive Identification of FIR Systems With Binary-Valued Observations

This paper considers the identification problem of finite impulse response (FIR) systems with binary-valued observations under the assumption of fixed threshold and bounded persistently excitations. A recursive projection algorithm is constructed to estimate the unknown parameter. For first-order FIR systems, the convergence properties of the algorithm are analyzed theoretically. With mild conditions on the weight coefficients in the parameter update, the algorithm is proved to be convergent in mean square and the convergence rate can be the reciprocal of the number of observations, which has the same order as the optimal estimation when the system output is exactly known. Furthermore, it is also shown that the Cramér-Rao (CR) lower bound is achieved asymptotically with proper weight coefficients, which indicates that the algorithm is optimal in the sense of asymptotic efficiency. Some numerical examples are simulated to demonstrate the effectiveness of the proposed algorithm in both first-order and high-order FIR systems.
Knowledge Management and Its Impacts on Organisational Performance: An Empirical Research in Public Higher Education Institutions of Vietnam

Studies that acknowledge the association between knowledge management (KM) and organisational performance (OP) are replete in extant literature. However, there are few of them providing empirical evidence of this relationship especially in academia. Multiple conjectures are made about this little-known relationship. This paper aims to explore the impacts of KM on OP in the public higher education institutions (HEIs) of Vietnam. Results of the studies were generated based on survey data collected in 30 universities located in three regions of Vietnam during 2017 by means of structured questionnaire. The hypothesised relationships between KM and OP are tested using structural equation modelling (SEM). Findings of the research showcase that KM does exert its impacts but not on all dimensions of OP in the public HEIs of Vietnam. Besides adding to the literature on performance management in academia by demonstrating the impacts of KM as a managerial tool for increasing OP, the paper also presents measurement dimensions with specific scale items measuring KM and OP of a HEI. Avenues for future research are also recommended.
A New Two-Dimensional Mutual Coupled Logistic Map and Its Application for Pseudorandom Number Generator

Given that the sequences generated by logistic map are unsecure with a number of weaknesses, including its relatively small key space, uneven distribution, and vulnerability to attack by phase space reconstruction, this paper proposes a new two-dimensional mutual coupled logistic map, which can overcome these weaknesses. Our two-dimensional chaotic map model is simpler than the recently proposed three-dimensional coupled logistic map, whereas the sequence generated by our system is more complex. Furthermore, a new kind of pseudorandom number generator (PRNG) based on the mutual coupled logistic maps is proposed for application. Both statistical tests and security analysis show that our proposed PRNG has good randomness and that it can resist all kinds of attacks. The algorithm speed analysis indicates that PRNG is valuable to practical applications.
Time- and space-efficient eye tracker calibration

One of the obstacles to bring eye tracking technology to everyday human computer interactions is the time consuming calibration procedure. In this paper we investigate a novel calibration method based on smooth pursuit eye movement. The method uses linear regression to calculate the calibration mapping. The advantage is that users can perform the calibration quickly in a few seconds and only use a small calibration area to cover a large tracking area. We first describe the theoretical background on establishing a calibration mapping and discuss differences of calibration methods used. We then present a user study comparing the new regression-based method with a classical nine-point and with other pursuit-based calibrations. The results show the proposed method is fully functional, quick, and enables accurate tracking of a large area. The method has the potential to be integrated into current eye tracking systems to make them more usable in various use cases.
A low dimensional descriptor for detection of anomalies in crowd videos

In this paper a novel descriptor is proposed for anomaly detection in crowd videos at a global level. Traditional approaches for anomaly detection in crowd videos face the dilemma of trade-off between high accuracy and real time performance. In order to resolve this issue, we propose an efficient descriptor composed of three different features extracted from the optical flow (OF) of a video sequence. The first feature is the sum of the optical flow field magnitude computed after applying a threshold. The second feature is the joint entropy of the OF magnitude of two consecutive frames used to measure the dissimilarity. The third feature is the variance computed from a space–time cuboid constructed using history of the OF field magnitude. Performance of the proposed descriptor is evaluated on the widely used UMN dataset in terms of accuracy and processing time. For UMN dataset, the proposed descriptor provides the highest area under the curve (AUC) compared to the approaches already published in literature.
AtICNet: semantic segmentation with atrous spatial pyramid pooling in image cascade network

This paper describes a new type of image segmentation method based on deep convolutional neural networks (DCNN) in the actual autonomous driving scene. The spatial pyramid pooling model is used to identify and segment the actual scene to complete the machine-aware task. In order to improve the information aggregation of the whole image, we use atrous convolution for multi-scale feature extraction based on the pyramid structure of image cascade network (ICNet), removing a residual module in the fifth stage of the network, in order to reduce the scale of the convolutional layer. The feature map is preprocessed by padding and atrous convolution before the four-level spatial pyramid model. Then, conventional convolutions are introduced to compose the atrous spatial pyramid pooling (ASPP) structure. Finally, the four feature maps in the pyramid are merged with the feature maps before input into the pyramid. This paper analyzes the spatial pyramid model, receptive field, and dilation convolution in detail and propose atrous image cascade network (AtICNet). Experiment results in the cityscape dataset have shown that AtICNet has some improvements over ICNet, by improving the accuracy of the segmentation.
A Controlled Experiment with Novice Developers on the Impact of Task Description Granularity on Software Quality in Test-Driven Development

Background: Test-Driven Development (TDD) is an iterative software development process characterized by test-code-refactor cycle. TDD recommends that developers work on small and manageable tasks at each iteration. However, the ability to break tasks into small work items effectively is a learned skill that improves with experience. In experimental studies of TDD, the granularity of task descriptions is an overlooked factor. In particular, providing a more granular task description in terms of a set of sub-tasks versus providing a coarser-grained, generic description. Objective: We aim to investigate the impact of task description granularity on the outcome of TDD, as implemented by novice developers, with respect to software quality, as measured by functional correctness and functional completeness. Method: We conducted a one-factor crossover experiment with 48 graduate students in an academic environment. Each participant applied TDD and implemented two tasks, where one of the tasks was presented using a more granular task description. Resulting artifacts were evaluated with acceptance tests to assess functional correctness and functional completeness. Linear mixed-effects models (LMM) were used for analysis. Results: Software quality improved significantly when participants applied TDD using more granular task descriptions. The effect of task description granularity is statistically significant and had a medium to large effect size. Moreover, the task was found to be a significant predictor of software quality which is an interesting result (because two tasks used in the experiment were considered to be of similar complexity). Conclusion: For novice TDD practitioners, the outcome of TDD is highly coupled with the ability to break down the task into smaller parts. For researchers, task selection and task description granularity requires more attention in the design of TDD experiments. Task description granularity should be taken into account in secondary studies. Further comparative studies are needed to investigate whether task descriptions affect other development processes similarly.
Optimizing Declarative Graph Queries at Large Scale

This paper presents GraphRex, an efficient, robust, scalable, and easy-to-program framework for graph processing on datacenter infrastructure. To users, GraphRex presents a declarative, Datalog-like interface that is natural and expressive. Underneath, it compiles those queries into efficient implementations. A key technical contribution of GraphRex is the identification and optimization of a set of global operators whose efficiency is crucial to the good performance of datacenter-based, large graph analysis. Our experimental results show that GraphRex significantly outperforms existing frameworks---both high- and low-level---in scenarios ranging across a wide variety of graph workloads and network conditions, sometimes by two orders of magnitude.
Cross-Modality Personalization for Retrieval

Existing captioning and gaze prediction approaches do not consider the multiple facets of personality that affect how a viewer extracts meaning from an image. While there are methods that consider personalized captioning, they do not consider personalized perception across modalities, i.e. how a person's way of looking at an image (gaze) affects the way they describe it (captioning). In this work, we propose a model for modeling cross-modality personalized retrieval. In addition to modeling gaze and captions, we also explicitly model the personality of the users providing these samples. We incorporate constraints that encourage gaze and caption samples on the same image to be close in a learned space; we refer to this as content modeling. We also model style: we encourage samples provided by the same user to be close in a separate embedding space, regardless of the image on which they were provided. To leverage the complementary information that content and style constraints provide, we combine the embeddings from both networks. We show that our combined embeddings achieve better performance than existing approaches for cross-modal retrieval.
A sheared low-rank model for oblique stripe removal

Stripe noise removal from remote sensing imagery is a key preprocessing step for many applications, such as urban planning, military, and environmental monitoring. Two significant priors of the stripe are low-rankness and group sparsity, which play a central role in many destriping methods. However, for oblique stripes, these two priors are no longer explicitly applicable. To overcome this difficulty, we propose a novel sheared decomposition framework for oblique stripe removal. The main idea is to perform a shear operation on the observed image, so that the oblique stripe is transformed to nearly vertical lines. For the stripe component, we characterize the low-rankness and group sparsity priors in the sheared domain. For the image component, we characterize the smoothness prior in the original domain by unidirectional total variation (UTV), which can preserve the strong edges and geometric features while suppressing stripes. To solve the proposed model, we design an efficient alternating direction method of multipliers (ADMM) algorithm with guaranteed convergence. Experiments using simulated and real data show that the proposed method can remove the oblique stripe effectively and outperform the state-of-the-art methods qualitatively and quantitatively.
A Mixed Integer Linear Programming Formulation to Artificial Neural Networks

This paper studies the problem of computing an input vector for a given pair of Artificial Neural Network (ANN) and output vector, which is a kind of inverse problem. This problem has potential applications in design of new objects, especially in design of new chemical compounds. This paper focuses on ANNs in which activation functions are represented as continuous piece-wise linear functions, which can exactly represent ReLU functions and well approximate sigmoid functions. It is shown that this inverse problem can be formulated as a Mixed Integer Linear Programming Problem (MILP) with O(|V | + nb) variables and constraints, where V is a set of neurons in a given ANN and nb denotes the total number of break points over all activation functions fv, v ϵ V.
If I were a grown-up: Children’s response to the rubber hand illusion with different hand sizes

During childhood, children's bodies undergo rapid physical growth, which may affect their ability to accurately perceive their own bodies as well as the external environment. Concurrently, multisensory processes underlying bodily self-consciousness gradually develop. However, little is known of the relation between changes in body size and corresponding update in bodily self-consciousness. In this study, 6- to 8-year-old children experienced the "rubber hand illusion" while watching a regular (child-like) or larger (adult-like) rubber hand being touched either synchronously or asynchronously with their own real hand. We measured proprioceptive drift and subjective ownership as well as changes in weight estimation of objects. Synchronous versus asynchronous visuotactile stroking with both the regular and larger hands caused a change in subjective body ownership but did not make a difference in perceived hand location and weight estimation of objects having the same size but different weight. Intriguingly, simply viewing the regular hand led to greater proprioceptive drift and to overestimation of the objects' weight relative to the larger hand. In addition, weight estimation was also influenced by hand size after visuotactile stroking. These results demonstrate that visuotactile information modulates the intrinsic properties of body representation in terms of size in children and further corroborate previous findings suggesting late developing optimal calibration of visual and proprioceptive signals.
Combining data analytics and developers feedback for identifying reasons of inaccurate estimations in agile software development

Background: Effort estimations are critical tasks greatly influencing the accomplishment of software projects. Despite their recognized relevance, little is yet known what indicators for inaccurate estimations exist, and which are the reasons of inaccurate estimations. Aims: In this manuscript, we aim at contributing to this existing gap. To this end, we implemented a tool that combines data analytics and developers’ feedback, and we employed that tool in a study. In that study, we explored the most common reasons of inaccurate user story estimations and the possible indicators of inaccurate estimations. Method: We relied on a mixed method approach used to study reasons and indicators for the identification and prediction of inaccurate estimations in practical agile software development contexts. Results: Our results add to the existing body of knowledge in multiple ways. We elaborate causes for inaccurate estimations going beyond the borders of existing literature; for instance, we show that lack of developers’ experience is the most common reason of inaccurate estimations. Further, our results suggest, for example, that the higher the complexity, the higher the uncertainty in the estimation. Conclusions: Overall, our results strengthen our confidence in the usefulness of using data analytics with human-in-the-loop mechanisms to improve effort estimations.
Blockchain and Smart Contracts

This paper presents an introduction to the current state of art of the Blockchain and Smart Contract technologies. Blockchain is a fast-disruptive technology becoming a key instrument in share economy. The Blockchain-based Smart Contract aim to automatically and securely execute the needed responsibilities of a contract without the support of a centralized execution authority. The Smart Contract runs on top of the Blockchain to facilitate, execute and enforce an agreement between un-trusted parties without the interfere of third party to trust it as this Smart Contract is an executable code that runs with rules on the Blockchain. Smart Contracts have some features that serve the goals of social justice and fairness. The paper presents the basic important information about the structures of the Blockchain and Smart Contract technologies and conduct a comparison between the different methodologies used in the Smart Contracts. The issues faced within the Smart Contract technology are surveyed. The four key issues are identified as: codifying, security, privacy and performance issues. We survey case cases of usage of the Blockchain in various business sectors like real estate, voting system and supply chain. The paper aims to assist a developer to grasp the big picture of the Blockchain technology and to further assist in the decision process of suitability of the technology to a specific application area.
Performance Evaluation of Hyperledger Fabric with Malicious Behavior

Hyperledger Fabric is a widely-used permissioned blockchain platform for enterprise consortium applications. It adopts Practical Byzantine Fault Tolerance (PBFT) algorithm as the consensus protocol in its version 0.6. Faulty replicas could intentionally delay messages, be not responsive and send inconsistent messages to different replicas. Faulty clients and replicas could also launch denial-of-service attack to make resources unavailable. The malicious behavior significantly undermines the system. However, the existing performance evaluation for Fabric is accomplished in a fault-free environment without malicious behaviors. In this paper, we analyze the impact of malicious behavior, design malicious behavior patterns and test the blockchain performance on Hyperledger Fabric.
User-Oriented Paraphrase Generation With Keywords Controlled Network

Paraphrase generation can help with both downstream tasks in natural language processing (NLP) and human writing in our daily life. Most of the prevalent neural models focus on the former usages and generate uncontrolled paraphrase while they ignore the subtleties of users' requirement. In addition, the existing tools for users are usually rule-based which is unnatural due to the complexity of the paraphrase nature. To this end, we propose a keyword controlled network (KCN) which can be used as an assistant paraphrase generation tool. The KCN works in an interactive manner and generates different paraphrases given different keywords. The model is based on a Sequence-to-Sequence (Seq2Seq) framework integrated with copy mechanism. Given the source sentence and the keywords, two encoders transform them into vector representations. Then, the representations are fused together and used for the decoder. The decoder with attention mechanism either copies the words from the keywords or generates words from the whole dictionary. In the training stage, as the source sentence and the target sentence are all valid paraphrases, the model is trained to generate each given different keywords, which simulates the behaviors of users. The extensive experiments on three datasets show that our method outperforms baselines in the automatic evaluation (0.06 absolute improvement in BLEU) and the generated paraphrases meet user expectation in the human evaluation.
Using Collaborative Filtering Algorithms Combined with Doc2Vec for Movie Recommendation

Information recommendation methods mainly include collaborative filtering and content-based. The collaborative filtering method is the most widely used recommendation method. It mainly uses the preferences of a group with similar interest or shared experience to recommend information of interest to users, but it will encounter serious data sparseness and cold start problems. In this paper, we propose a film recommendation model based on word vector features. The Doc2Vec model is used to extract the semantics, grammar and word order of the sentence, transform it into a fixed dimension vector, and the similarity of the vector will be calculated and applied to the collaborative filtering recommendation algorithm. Experiments show that the recommendation results are improved in both accuracy and recall.