Reviewerly/exHarmony huggingface dataset:
A survey on construction and enhancement methods in service chatbots design

Chatbots are being widely applied in many service industries to help schedule meetings, online shopping, restaurant reservations, customer care and so on. The key to the success of the service chatbots design is to provide satisfying responses to the given user's requests. This survey aims to provide a comprehensive review of chatbots construction and enhancement methods. We first introduce major techniques for the three core design philosophies, which are rule-based, retrieval-based and generation-based methods, followed by a brief summary of the evaluation metrics. Then we present methods to enhance service chatbot's capabilities with either an ensemble of multiple chatbots, collaborating with human workers or learning from users. Finally, in future directions we discuss the promising response generation models for chatbots using the recent progress in the transformer and contextual embeddings, as well as potential ways to construct a chatbot with personality to achieve a better user experience.
Reveal-More: Amplifying Human Effort in Quality Assurance Testing Using Automated Exploration

Attempting to maximize coverage of a game via human gameplay is laborious and repetitive, introducing delays in the development process. Despite the importance of quality assurance (QA) testing, QA remains an underinvested area in the technical games research community. In this paper, we show that relatively simple automatic exploration techniques can be used to multiplicatively amplify coverage of a game starting from human tester data. Instead of attempting to displace human QA efforts, we seek to grow the impact that a human tester can make. Experiments with two games for the Super Nintendo Entertainment System highlight the qualitative and quantitative differences between isolated human and machine play compared to our hybrid approach called Reveal-More. We contribute a QA testing workflow that scales with the amount of human and machine time allocated to the effort.
Negation and speculation scope detection using recursive neural conditional random fields

Negation and speculation scope detection is an important task in natural language processing. Previous studies all show that syntactic information is crucial to the task. However, these work mainly focuses on human-designed discrete features and local features extracted from dependency tree, limiting the performance of the task. In this paper, we propose a recursive neural network sequence labeling model, representing whole dependency tree globally and learning automatically syntactic features, for the task. Specifically, recursive neural network first learns a high-level representation for the words in the context of each sentence, and captures the cue word with its target scope through the global dependency structure. Then, CRFs layer takes the representation from recursive neural network as input to jointly decode labels for the whole sentence. Experimental results on English dataset BioScope and Chinese dataset CNeSp show that our model outperforms the state-of-the-art systems.
GASP Codes for Secure Distributed Matrix Multiplication

We consider the problem of secure distributed matrix multiplication (SDMM) in which a user wishes to compute the product of two matrices with the assistance of honest but curious servers. We construct polynomial codes for SDMM by studying a combinatorial problem on a special type of addition table, which we call the degree table. The codes are based on arithmetic progressions, and are thus named GASP (Gap Additive Secure Polynomial) Codes. GASP Codes are shown to outperform all previously known polynomial codes for secure distributed matrix multiplication in terms of download rate.
A fine-grained authorized keyword secure search scheme with efficient search permission update in cloud computing

With the rapid development of cloud computing, secure search has become a hot research spot, which is a promising technique that allows a data user to perform privacy-preserving keyword-based search over encrypted cloud data. In this paper, we further consider the secure search problem based on a practical application scenario that a data owner needs to grant different keyword query permissions for different data users to achieve flexible access control on outsourced encrypted data in the cloud computing environment. To address this problem, we propose a fine-grained authorized keyword secure search scheme by leveraging the ciphertext policy attribute-based encryption (ABE), which not only supports privacy-preserving keyword-based search over encrypted data, but also inherits flexible and fine-grained data privilege control properties of ABE. Moreover, our proposed scheme is able to achieve fine-grained search permission update with very small communication and computation cost. By running the attribute revocation sub-protocol and attribute addition sub-protocol, the data owner can flexibly and efficiently update a data user’s keyword search permissions when the data user’s system role changes. We provide detailed performance analysis and rigorous security proof for our scheme. Extensive experiments demonstrate the correctness and practicality of the proposed scheme.
What Empirically Based Research Tells Us About Game Development

Abstract This paper reviews empirically grounded research on practices in game development with the intent to give a comprehensive overview of contemporary development practices used in the video game industry. While there are many intangible elements that inform game development processes, this review specifically covers the more immediate practical challenges. The review covers a total of 48 papers published between 2006 and 2016, which were all subjected to thematic analysis by three reviewers. The results of the review show that an almost universal characteristic of game development is that it is almost impossible to accurately plan a development project in detail, largely due to the soft requirements inherent in game production which emerge mid-process during development projects, during when testing is coupled with continuous ideation and refinement. Practicing game developers have created their own frameworks that accommodate for this lack of planning. They include flat hierarchies, democratic decision-making, creative autonomy, and informal communication, which are designed to create an environment that maintains creativity and openness to product changes long into the production process. These frameworks vary significantly between studios and often between individual projects. This review also shows that the term ‘Agile’, while often used by both researchers and developers to characterize the process of game development, is not an apt descriptor of how game developers actually work. Agile is used as shorthand for unstructured and flexible development, rather than serving as a descriptor of a definable or unified work method. Finally, as companies develop more complicated hierarchies of stakeholders and staff, the desired flexibility and autonomy of game development becomes increasingly complicated to maintain, and often necessitates more formalized management processes and company structures. In these cases, inherent tensions of game development become more pronounced, and continuous creativity is hard to maintain due to a growing need to formalize processes.
Private Computation with Side Information: The Single-Server Case

This paper considers the problem of single-server Private Computation wit Side Information (PC-SI). In this problem, there is a user that initially has a subset of M messages from a database stored on a single server, where the identities of the side information messages are initially unknown to the server. The user wishes to compute a linear combination of a subset of D messages (disjoint from the set of side information messages) while protecting the identities of the messages in the demanded linear combination. The objective of the user is to minimize the download cost, which is defined as the total amount of information that the user downloads from the server.We establish a lower bound on the capacity of the PC-SI problem, where the capacity is defined as the supremum of all achievable download rates. The proof relies on a novel achievability scheme which combines together the ideas of the interference alignment and the Partition and Code scheme previously introduced for private information retrieval with side information. In addition, for the case of M = 1 and D = 2, we prove the tightness of the rate achievable by the proposed scheme, when we restrict ourselves to the scalar-linear PC-SI schemes. The proof of converse is based on a combination of new algebraic and information-theoretic arguments.
Future Skills of Design for Sustainability: An Awareness-Based Co-Creation Approach

Our inner, invisible dimension consisting of our values, mental models and worldviews, has been identified as a significant leverage point for transformational change, as it brings to life our actions. Accordingly, the inner dimension of sustainability has a major role in transitioning towards desirable and sustainable futures. This paper focuses on exploring what kind of methods and competences are needed to access and work with the inner dimension as part of collaborative design practices aiming for sustainable and deep change. Thus, a lesser researched, alternative perspective to design discourse, the awareness-based co-creation approach is highlighted as a potential and emerging direction for design for sustainability. By thinking across: (1) literature findings of the concept of inner dimension of sustainability; (2) existing knowledge of awareness-based transformation approaches and (3) results of an experimental case study done in the context of nature tourism, it was recognized that more examples are needed on how to enable, promote and capture participants’ transformative experiences. Finally, the argument is made that awareness-based competencies should be considered as essential future skills and competences of design for sustainability.
Köppen-Geiger climate classification adjustment of the BRL diffuse irradiation model for Australian locations

Numerous mathematical models have been developed to estimate diffuse and direct irradiance components based on global irradiation measurements. The Boland–Ridley–Lauret (BRL) model consists of a single set of parameters for all global locations. There is scope to improve the BRL model to better match local climatic conditions. In this research, the Köppen-Geiger climate classification system is considered to develop a set of adjusted BRL models for Australian conditions. Ground-based and satellite-based irradiation data derived from the Australian Bureau of Meteorology are used to tune and test new BRL models developed at a national level and for each climate zone. Irradiation data are processed through a rigorous quality control procedure before parameter tuning. For ground-based data, a new national model results in an improvement in 96% of statistical indicators over the original BRL model while Köppen-Geiger zone adjusted models show improvement over the new national model in 72% of the statistics. For satellite-based global irradiation estimates, a new national BRL model also results in observed improvements, however, no discernible improvement is observed for Köppen-Geiger zone models.
Combining Domain Knowledge Extraction With Graph Long Short-Term Memory for Learning Classification of Chinese Legal Documents

It is of great importance for procedure retrieval to find an effective classification method of Chinese legal documents with deep semantic understanding, as the electronic documents of Chinese law have massive volume and complex structure. In this paper, a method for learning Chinese legal document classification using Graph LSTM (Long Short-Term Memory) combined with domain knowledge extraction is proposed. First, the judicial domain model is constructed based on ontologies that include top-level ontology and domain-specific ontology. Second, the legal documents are divided into different knowledge blocks through top-level ontology and domain-specific ontology. Third, information is extracted from the knowledge blocks according to the legal domain model and stored in XML files. At last, Graph LSTM is applied for classification. The experiments show that compared with the traditional classification methods of support vector machine (SVM) and LSTM, Graph LSTM has higher classification accuracy and better classification performance.
Analyzing the Influence of IoT in Tourism Industry

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Improving the Accuracy and Robustness of Ultra-Wideband Localization Through Sensor Fusion and Outlier Detection

This article presents sensor fusion techniques for ultra-wideband-based localization to achieve sufficient accuracy and robustness for the control of vehicles in an industrial environment. We propose two outlier detection methods in combination with an extended Kalman Filter, and present experimental validation where 10 cm accuracy is achieved even in difficult NLOS conditions.
Enhancing the security of patients’ portals and websites by detecting malicious web crawlers using machine learning techniques

There is increasing demand for access to medical information via patients' portals. However, one of the challenges towards widespread utilisation of such service is maintaining the security of those portals. Recent reports show an alarming increase in cyber-attacks using crawlers. These software programs crawl web pages and are capable of executing various commands such as attacking web servers, cracking passwords, harvesting users' personal information, and testing the vulnerability of servers. The aim of this research is to develop a new effective model for detecting malicious crawlers based on their navigational behavior using machine-learning techniques.In this research, different methods of crawler detection were investigated. Log files of a sample of compromised web sites were analysed and the best features for the detection of crawlers were extracted. Then after testing and comparing several machine learning algorithms including Support Vector Machine (SVM), Bayesian Network and Decision Tree, the best model was developed using the most appropriate features and its accuracy was evaluated.Our analysis showed the SVM-based models can yield higher accuracy (f-measure = 0.97) comparing to Bayesian Network (f-measure = 0.88) and Decision Tree (f-measure = 0.95) and artificial neural network (ANN) (f-measure = 0.87)for detecting malicious crawlers. However, extracting proper features can increase the performance of the SVM (f-measure = 0.98), the Bayesian network (f-measure = 0.94) and the Decision Tree (f-measure = 0.96) and ANN (f-measure = 0.92).Security concerns are among the potential barriers to widespread utilisation of patient portals. Machine learning algorithms can be accurately used to detect malicious crawlers and enhance the security of sensitive patients' information. Selecting appropriate features for the development of these algorithms can remarkably increase their accuracy.
A Game-Based Combinatorial Double Auction Model for Cloud Resource Allocation

Cloud computing integrates a large number of resources through virtualization technology, and then provides users with personalized services on an on-demand basis. In response to this service model, this paper draws on the economic theories and proposed a game-based combinatorial double auction model for cloud resource allocation. Firstly, through Harsanyi transformation, the incomplete information game for cloud resource allocation is converted into a complete but imperfect information game, and the Bayesian Nash equilibrium solution is obtained. Then, we designed the resource allocation model supporting multiple infrastructure providers and service providers bidding on various combinations of resources. Considering both parties' interests, this model ensures fairness and high resource utilization. Simulation results show that this method not only forms a fair incentive mechanism for all parties in the transaction, but also optimizes the social welfare.
RBPNET: An asymptotic Residual Back-Projection Network for super-resolution of very low-resolution face image

The super-resolution of a very low-resolution face image is a challenge task in single image super-resolution. Most of deep learning methods learn a non-linear mapping of input-to-target space by one-step upsampling. These methods are difficult to reconstruct a high-resolution face image from single very low-resolution face image. In this paper, we propose an asymptotic Residual Back-Projection Network (RBPNet) to gradually learn residual between the reconstructed face image and the ground truth by multi-step residual learning. Firstly, the reconstructed high-resolution feature map is projected to the original low-resolution feature space to generate low-resolution feature map (the projected low-resolution feature map). Secondly, the projected low-resolution feature map is subtracted by original feature map to generate low-resolution residual feature map. And finally, the low-resolution residual feature map is mapped to high-resolution feature space. The network will get a more accurate high-resolution image by iterative residual learning. Meanwhile, we explicitly reconstruct the edge map of face image and embed it into the reconstruction of high-resolution face image to reduce distortion of super-resolution results. Extensive experiments demonstrate the effectiveness and advantages of our proposed RBPNet qualitatively and quantitatively.
Using theoretical ROC curves for analysing machine learning binary classifiers

Most binary classifiers work by processing the input to produce a scalar response and comparing it to a threshold value. The various measures of classifier performance assume, explicitly or implicitly, probability distributions Ps and Pn of the response belonging to either class, probability distributions for the cost of each type of misclassification, and compute a performance score from the expected cost. In machine learning, classifier responses are obtained experimentally and performance scores are computed directly from them, without any assumptions on Ps and Pn. Here, we argue that the omitted step of estimating theoretical distributions for Ps and Pn can be useful. In a biometric security example, we fit beta distributions to the responses of two classifiers, one based on logistic regression and one on ANNs, and use them to establish a categorisation into a small number of classes with different extremal behaviours at the ends of the ROC curves.
Parameter Setting for Deep Neural Networks Using Swarm Intelligence on Phishing Websites Classification

Over the past years, the application of deep neural networks in a wide range of areas is noticeably increasing. While many state-of-the-art deep neural networks are providing the performance comparable or in some cases even superior to humans, major challenges such as parameter settings for learning deep neural networks and construction of deep learning architectures still exist. The implications of those challenges have a significant impact on how a deep neural network is going to perform on a specific task. With the proposed method, presented in this paper, we are addressing the problem of parameter setting for a deep neural network utilizing swarm intelligence algorithms. In our experiments, we applied the proposed method variants to the classification task for distinguishing between phishing and legitimate websites. The performance of the proposed method is evaluated and compared against four different phishing datasets, two of which we prepared on our own. The results, obtained from the conducted empirical experiments, have proven the proposed approach to be very promising. By utilizing the proposed swarm intelligence based methods, we were able to statistically significantly improve the predictive performance when compared to the manually tuned deep neural network. In general, the improvement of classification accuracy ranges from 2.5% to 3.8%, while the improvement of F1-score reached even 24% on one of the datasets.
Synthetic-to-Real Domain Adaptation for Object Instance Segmentation

Object instance segmentation can achieve preferable results, powered with sufficient labeled training data. However, it is time-consuming for manually labeling, leading to the lack of large-scale diversified datasets with accurate instance segmentation annotations. Exploiting the synthetic data is a very promising solution except for domain distribution mismatch between synthetic dataset and real dataset. In this paper, we propose a synthetic-to-real domain adaptation method for object instance segmentation. At first, this approach is trained to generate object detection and segmentation using annotated data from synthetic dataset. Then, a feature adaptation module (FAM) is applied to reduce data distribution mismatch between synthetic dataset and real dataset. The FAM performs domain adaptation from three different aspects: global-level base feature adaptation module, local-level instance feature adaptation module, and subtle-level mask feature adaptation module. It is implemented based on novel discriminator networks with adversarial learning. The three modules of FAM have positive effects on improving the performance when adapting from synthetic to real scenes. We evaluate the proposed approach on Cityscapes dataset by adapting from Virtual KITTI and SYNTHIA datasets. The results show that it achieves a significantly better performance over the state-of-the-art methods.
The last entrance plane method for contact indeterminacy between convex polyhedral blocks

This study proposes the last entrance plane (LEP) principle to identify the physical contact plane, which is applicable to resolve contact indeterminacy between convex polyhedral blocks. Based on contact theory, the LEP principle reveals that the physical contact point lies on the last entrance plane. The LEP method is developed to identify the physical contact plane before penetration considering the geometrical and kinematical information. It offers a unified solution to contact indeterminacy between 2D convex polygonal and 3D convex polyhedral blocks. Using discontinuous deformation analysis, several numerical tests are evaluated to validate the accuracy and robustness of the LEP method.
A GAN Model With Self-attention Mechanism To Generate Multi-instruments Symbolic Music

GAN has recently been proved to be able to generate symbolic music in the form of piano-rolls. However, those existing GAN-based multi-track music generation methods are always unstable. Moreover, due to defects in the temporal features extraction, the generated multi-track music does not sound natural enough. Therefore, we propose a new GAN model with self-attention mechanism, DMB-GAN, which can extract more temporal features of music to generate multi-instruments music stably. First of all, to generate more consistent and natural single-track music, we introduce self-attention mechanism to enable GAN-based music generation model to extract not only spatial features but also temporal features. Secondly, to generate multi-instruments music with harmonic structure among all tracks, we construct a dual generative adversarial architecture with multi-branches, each branch for one track. Finally, to improve generated quality of multi-instruments symbolic music, we introduce switchable normalization to stabilize network training. The experimental results show that DMB-GAN can stably generate coherent, natural multi-instruments music with good quality.
The impact on people’s holding intention of bitcoin by their perceived risk and value

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