Reviewerly/exHarmony huggingface dataset:
Orchestrating Game Generation

The design process is often characterized by and realized through the iterative steps of evaluation and refinement. When the process is based on a single creative domain such as visual art or audio production, designers primarily take inspiration from work within their domain and refine it based on their own intuitions or feedback from an audience of experts from within the same domain. What happens, however, when the creative process involves more than one creative domain such as in a digital game? How should the different domains influence each other so that the final outcome achieves a harmonized and fruitful communication across domains? How can a computational process orchestrate the various computational creators of the corresponding domains so that the final game has the desired functional and aesthetic characteristics? To address these questions, this paper identifies game facet orchestration as the central challenge for artificial-intelligence-based game generation, discusses its dimensions, and reviews research in automated game generation that has aimed to tackle it. In particular, we identify the different creative facets of games, propose how orchestration can be facilitated in a top-down or bottom-up fashion, review indicative preliminary examples of orchestration, and conclude by discussing the open questions and challenges ahead.
Searching for questions, original thoughts, or advancing theory: Human-machine communication

The study of Human-Machine Communication (HMC) has the potential to impact a variety of contexts and influence multiple aspects of everyday life. Robots and other machine communicators are increasingly performing social and workplace roles such as teachers, caregivers, surveillance, decision-makers and personal companionship. They have the ability to improve quality of human life through assistance, enabling for instance, independent living or providing support in work-intensive, difficult and possibly complex situations. Moreover, they have the ability to fill social roles. With social machines becoming more common, research into Human-Machine Communication is also expanding. Issues of how HMC is situated in the academic discussion alongside of the role of theory and directions concerning future research are offered.
Learning Cross-Modality Representations From Multi-Modal Images

Machine learning algorithms can have difficulties adapting to data from different sources, for example from different imaging modalities. We present and analyze three techniques for unsupervised cross-modality feature learning, using a shared autoencoder-like convolutional network that learns a common representation from multi-modal data. We investigate a form of feature normalization, a learning objective that minimizes cross-modality differences, and modality dropout, in which the network is trained with varying subsets of modalities. We measure the same-modality and cross-modality classification accuracies and explore whether the models learn modality-specific or shared features. This paper presents experiments on two public data sets, with knee images from two MRI modalities, provided by the Osteoarthritis Initiative, and brain tumor segmentation on four MRI modalities from the BRATS challenge. All three approaches improved the cross-modality classification accuracy, with modality dropout and per-feature normalization giving the largest improvement. We observed that the networks tend to learn a combination of cross-modality and modality-specific features. Overall, a combination of all three methods produced the most cross-modality features and the highest cross-modality classification accuracy, while maintaining most of the same-modality accuracy.
PDLM: Privacy-Preserving Deep Learning Model on Cloud with Multiple Keys

Deep learning has aroused a lot of attention and has been used successfully in many domains, such as accurate image recognition and medical diagnosis. Generally, the training of models requires large, representative datasets, which may be collected from a large number of users and contain sensitive information (e.g., users' photos and medical information). The collected data would be stored and computed by service providers (SPs) or delegated to an untrusted cloud. The users can neither control how it will be used, nor realize what will be learned from it, which make the privacy issues prominent and severe. To solve the privacy issues, one of the most popular approaches is to encrypt users' data with their public keys. However, this technique inevitably leads to another challenge that how to train the model based on multi-key encrypted data. In this paper, we propose a novel privacy-preserving deep learning model, namely PDLM, to apply deep learning over the encrypted data under multiple keys. In PDLM, lots of users contribute their encrypted data to SP to learn a specific model. We adopt an effective privacy-preserving calculation toolkit to achieve the training process based on stochastic gradient descent (SGD) in a privacy-preserving manner. We also prove that our PDLM can achieve users' privacy preservation and analyze the efficiency of PDLM in theory. Finally, we conduct an experiment to evaluate PDLM over two real-world datasets and empirical results demonstrate that our PDLM can effectively and efficiently train the model in a privacy-preserving way.
Quantifying cloud elasticity with container-based autoscaling

Containers have been a pervasive approach to help rapidly develop, test and update the Internet of Things applications (IoT). The autoscaling of containers can adaptively allocate computing resources for various data volumes over time. Therefore, elasticity, a critical feature of a cloud platform, is significant to measure the performance of lightweight containers. In this paper, we propose a framework with container auto-scaler. It monitors containers resource usage and accordingly scales in or scales out containers in need. Further, we define elasticity mathematically in order to quantify the cloud elasticity using the proposed framework. Extensive experiments are carried out with different workload modes, workload durations, and scaling cool-down period of times. Experiment results show that the framework captures the workload variation firmly with a very short delay. We also find out that the cloud platform shows the best elasticity in repeat workload mode due to its recurring and predictable feature. Finally, we discover the length of the cool-down period should be properly set up in order to balance system stability and good elasticity.
A Systematic Literature Review on Bad Smells–5 W's: Which, When, What, Who, Where

Bad smells are sub-optimal code structures that may represent problems needing attention. We conduct an extensive literature review on bad smells relying on a large body of knowledge from 1990 to 2017. We show that some smells are much more studied in the literature than others, and also that some of them are intrinsically inter-related (which). We give a perspective on how the research has been driven across time (when). In particular, while the interest in duplicated code emerged before the reference publications by Fowler and Beck and by Brown et al., other types of bad smells only started to be studied after these seminal publications, with an increasing trend in the last decade. We analyzed aims, findings, and respective experimental settings, and observed that the variability of these elements may be responsible for some apparently contradictory findings on bad smells (what). Moreover, we could observe that, in general, papers tend to study different types of smells at once. However, only a small percentage of those papers actually investigate possible relations between the respective smells (co-studies), i.e., each smell tends to be studied in isolation. Despite of a few relations between some types of bad smells have been investigated, there are other possible relations for further investigation. We also report that authors have different levels of interest in the subject, some of them publishing sporadically and others continuously (who). We observed that scientific connections are ruled by a large “small world” connected graph among researchers and several small disconnected graphs. We also found that the communities studying duplicated code and other types of bad smells are largely separated. Finally, we observed that some venues are more likely to disseminate knowledge on Duplicate Code (which often is listed as a conference topic on its own), while others have a more balanced distribution among other smells (where). Finally, we provide a discussion on future directions for bad smell research.
Deep Variational and Structural Hashing

In this paper, we propose a deep variational and structural hashing (DVStH) method to learn compact binary codes for multimedia retrieval. Unlike most existing deep hashing methods which use a series of convolution and fully-connected layers to learn binary features, we develop a probabilistic framework to infer latent feature representation inside the network. Then, we design a struct layer rather than a bottleneck hash layer, to obtain binary codes through a simple encoding procedure. By doing these, we are able to obtain binary codes discriminatively and generatively. To make it applicable to cross-modal scalable multimedia retrieval, we extend our method to a cross-modal deep variational and structural hashing (CM-DVStH). We design a deep fusion network with a struct layer to maximize the correlation between image-text input pairs during the training stage so that a unified binary vector can be obtained. We then design modality-specific hashing networks to handle the out-of-sample extension scenario. Specifically, we train a network for each modality which outputs a latent representation that is as close as possible to the binary codes which are inferred from the fusion network. Experimental results on five benchmark datasets are presented to show the efficacy of the proposed approach.
Identification of fact-implied implicit sentiment based on multi-level semantic fused representation

Sentiment can be expressed in an explicit or implicit manner. Most of the current studies on sentiment analysis focus on the identification of explicit sentiment but ignore the implicit. According to our statistics during data labeling in previous work, nearly a third of subjective sentences contain implicit sentiment, and 72% of the implicit sentiment sentences are fact-implied ones. We analyze the characteristics of the sentences that express fact-implied implicit sentiment and consider that fact-implied implicit sentiment is usually affected by its sentiment target, context semantic background and its own sentence structure. This paper focuses on the recognition of fact-implied implicit sentiment at the sentence level. A multi-level semantic fusion method is proposed to learn the features for identification based on representation learning. Three features in different levels are learned from the corpus, namely, sentiment target representation at the word level, structure embedded representation at the sentence level and context semantic background representation at the document level. We manually construct a fact-implied implicit sentiment corpus in Chinese, and experiments on the datasets show that the proposed method can effectively recognize fact-implied implicit sentiment sentences.
Countering Selfish Mining in Blockchains

Selfish mining is a well known vulnerability in blockchains exploited by miners to steal block rewards. In this paper, we explore a new form of selfish mining attack that guarantees high rewards with low cost. We show the feasibility of this attack facilitated by recent developments in blockchain technology opening new attack avenues. By outlining the limitations of existing countermeasures, we highlight a need for new defense strategies to counter this attack, and leverage key system parameters in blockchain applications to propose an algorithm that enforces fair mining. We use the expected transaction confirmation height and block publishing height to detect selfish mining behavior and develop a network-wide defense mechanism to disincentivize selfish miners. Our design involves a simple modifications to transactions' data structure in order to obtain a “truth state” used to catch the selfish miners and prevent honest miners from losing block rewards.
Using chaotic maps for 3D boundary surveillance by quadrotor robot

Chaotic maps have been shown to be suitable for applications that require unpredictable behavior. In this paper, the chaotic maps are used for motion planning and control of a quadrotor for boundary surveillance purposes. The chaotic motion would prevent anticipation of the future movement of the quadrotor, especially in hostile situations where an intruding opponent exists. The chaotic trajectories are constructed based on a closed three-dimensional curve representing the boundary. It is demonstrated that the proposed algorithm for 3D planning generates chaotic paths by investigating the Lyapunov exponents. The desired unpredicted trajectory is used as input to an under-actuated control system. The performance of the algorithm is shown by simulation of the control system applied to the dynamic model.
Stochastic Blockchain for IoT Data Integrity

This paper proposes a blockchain-based data checking scheme to protect data integrity in Internet of Things (IoT). Traditional data integrity schemes such as symmetric key approaches and public key infrastructure (PKI) suffer from the single-point of failure and network congestion due to the centralized architecture. Motivated by the distributed data authentication in blockchain, we propose to adopt blockchain to ensure the data integrity in IoT networks. However, the existing blockchain scheme cannot be directly applied to IoT nodes with limited computing and network resources. Hence, we develop a stochastic blockchain scheme to limit the number of cooperative nodes and distribute the load to IoT edge nodes. In our scheme, the IoT data are broadcast by randomly selected cooperative nodes, thereby introducing uncertainty to the attacker and improving the system security level. Finally, we propose a lightweight mining process to make only the IoT edge nodes compete for block generation and share the block with other nodes. If our scheme is used in the case of having 9,000 legitimate nodes and 1,000 compromised nodes, only three cooperative nodes can achieve the probability of successful defense over 99 percent.
Detection of Multiclass Objects in Optical Remote Sensing Images

Object detection in complex optical remote sensing images is a challenging problem due to the wide variety of scales, densities, and shapes of object instances on the earth surface. In this letter, we focus on the wide-scale variation problem of multiclass object detection and propose an effective object detection framework in remote sensing images based on YOLOv2. To make the model adaptable to multiscale object detection, we design a network that concatenates feature maps from layers of different depths and adopt a feature introducing strategy based on oriented response dilated convolution. Through this strategy, the performance for small-scale object detection is improved without losing the performance for large-scale object detection. Compared to YOLOv2, the performance of the proposed framework tested in the DOTA (a large-scale data set for object detection in aerial images) data set improves by 4.4% mean average precision without adding extra parameters. The proposed framework achieves real-time detection for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024\times 1024$ </tex-math></inline-formula> image using Titan Xp GPU acceleration. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> <uri>https://github.com/WenchaoliuMUC/Detection-of-Multiclass-Objects-in-Optical-Remote-Sensing-Images</uri> </fn>
High resolution spatio-temporal modelling of solar photovoltaic potential for tropical islands: Case of Mauritius

Tropical and subtropical islands benefit from a wide spectrum of renewable energy resources and advancement in technology has made it feasible for harnessing these clean and abundant energy sources. Mauritius being located in the tropical belt, receives plenteous sunshine all year round. Consequently, to provide assistance to the Government and other relevant organizations wishing to deploy photovoltaic facilities on the island, the methodological framework and results presented have been organized to serve as a strong background to identify the optimum sites for efficient harnessing of solar energy through photovoltaic systems. The Multiple Criteria Decision Making (MCDM) analysis coupled to Analytical Hierarchy Process (AHP) have been used to model the solar photovoltaic potential of the island from a set of social, technical, legal, environmental and climatological factors. The performance of the model has been calibrated using the monthly fluctuations in electricity production from a photovoltaic power plant located on the island. We propose an adapted mathematical formula to theoretically estimate the electricity production from photovoltaic systems on the island on the temporal basis. The outcome of this research is the production of a high resolution yearly mean solar photovoltaic map alongside with the spatio-temporal variability of photovoltaic potential throughout the year.
Pixels and 3-D Points Alignment Method for the Fusion of Camera and LiDAR Data

The fusion of light detection and ranging (LiDAR) and camera data is a promising approach to improve the environmental perception and recognition for intelligent vehicles because of the combination of depth and color information. One of the difficulties in achieving the fusion is the accurate alignment of the 3-D points with the image pixels. Current methods of data alignment involve the steps of estimating the camera intrinsic parameters and developing a transformation matrix between the camera and LiDAR frame. The drawback of these methods is the accumulation of errors during the calculation of the camera intrinsic parameters and the transformation matrix. In order to improve the data alignment accuracy, we propose a novel algorithm that directly calculates the alignment between the 3-D points and the pixels without the need for camera parameters and calibration of the coordinate transformation matrix. We call the proposed method the pixel and 3-D point alignment (PPA) method. The alignment procedure is achieved by using the extracted corresponding points. First, we calculate a linear alignment matrix without considering the image distortion; and second, we optimize the parameters using the maximum likelihood estimation to consider the camera distortion. Simulation and experimental results indicate that the PPA method is able to align the 3-D points in LiDAR frame with the pixels in image frame with higher accuracy and increased robustness against noise in calibration process than comparable state-of-the-art methods.
A Multisensor Multiclassifier Hierarchical Fusion Model Based on Entropy Weight for Human Activity Recognition Using Wearable Inertial Sensors

Human activity recognition techniques based on wearable inertial sensors have achieved great success, but the classification accuracy of human activities using wearable sensors is not good enough in practice. In this paper, a multisensor multiclassifier hierarchical fusion model based on entropy weight for human activity recognition using wearable inertial sensors is proposed. The fusion model has two layers, including basic-classifier fusion layer and sensor fusion layer. The entropy weight method has been applied to achieve the weight values that can affect the decision results of each layer. In addition, a novel feature selection method based on congruent transformation in matrix is also proposed. Three major experiments have been conducted to reveal the feasibility and availability of our algorithms. The experiments show that our fusion algorithm may achieve the better recognition performance when compared with basic classifiers and majority voting. For different feature dimensions, the performance of our algorithm is also better than that of majority voting, and the recognition accuracy rate may reach 96.72%. In addition, the recognition accuracy rate of the proposed feature-selection method is about 96.96%, which is better than the other method.
Detecting terminological ambiguity in user stories: Tool and experimentation

Context. Defects such as ambiguity and incompleteness are pervasive in software requirements, often due to the limited time that practitioners devote to writing good requirements. Objective.We study whether a synergy between humans’ analytic capabilities and natural language processing is an effective approach for quickly identifying near-synonyms, a possible source of terminological ambiguity. Method.We propose a tool-supported approach that blends information visualization with two natural language processing techniques: conceptual model extraction and semantic similarity. We evaluate the precision and recall of our approach compared to a pen-and-paper manual inspection session through a controlled quasi-experiment that involves 57 participants organized into 28 groups, each group working on one real-world requirements data set. Results.The experimental results indicate that manual inspection delivers higher recall (statistically significant with p ≤ 0.01) and non-significantly higher precision. Based on qualitative observations, we analyze the quantitative results and suggest interpretations that explain the advantages and disadvantages of each approach. Conclusions.Our experiment confirms conventional wisdom in requirements engineering: identifying terminological ambiguities is time consuming, even when with tool support; and it is hard to determine whether a near-synonym may challenge the correct development of a software system. The results suggest that the most effective approach may be a combination of manual inspection with an improved version of our tool.
Ouroboros Crypsinous: Privacy-Preserving Proof-of-Stake

We present Ouroboros Crypsinous, the first formally analyzed privacy-preserving proof-of-stake blockchain protocol. To model its security we give a thorough treatment of private ledgers in the (G)UC setting that might be of independent interest. To prove our protocol secure against adaptive attacks, we introduce a new coin evolution technique relying on SNARKs and key-private forward secure encryption. The latter primitive-and the associated construction-can be of independent interest. We stress that existing approaches to private blockchain, such as the proof-of-work-based Zerocash are analyzed only against static corruptions.
Privacy Preserving Searchable Encryption with Fine-Grained Access Control

Searchable encryption facilitates cloud server to search over encrypted data without decrypting the data. Single keyword based searchable encryption enables a user to access a subset of documents, which contains the keyword of the user's interest. In this paper, we present a single keyword based searchable encryption scheme for the applications where multiple data owners upload their data and then multiple users can access the data. The scheme uses attribute based encryption that allows user to access the selective subset of data from cloud without revealing his/her access rights to the cloud server. The scheme is proven adaptively secure against chosen-keyword attack in the random oracle model. We have implemented the scheme on Google cloud instance and the performance of the scheme found practical in real-world applications.