Reviewerly/exHarmony huggingface dataset:
Regular Boardgames

We propose a new General Game Playing (GGP) language called Regular Boardgames (RBG), which is based on the theory of regular languages. The objective of RBG is to join key properties as expressiveness, efficiency, and naturalness of the description in one GGP formalism, compensating certain drawbacks of the existing languages. This often makes RBG more suitable for various research and practical developments in GGP. While dedicated mostly for describing board games, RBG is universal for the class of all finite deterministic turn-based games with perfect information. We establish foundations of RBG, and analyze it theoretically and experimentally, focusing on the efficiency of reasoning. Regular Boardgames is the first GGP language that allows efficient encoding and playing games with complex rules and with large branching factor (e.g. amazons, arimaa, large chess variants, go, international checkers, paper soccer).
Spatial distributions and the identification of ore-related anomalies of Cu across the boundary area of China and Mongolia

The 1:1, 000, 000 geochemical mapping across the boundary area of China and Mongolia has acquired high-quality basic geochemical data, yielding more than 9000 stream sediment samples and over 400 rock samples. Each stream sediment and rock sample site was assigned to the regional tectonic unit, geological background and geomorphic landscape maps, respectively, resulting in spatial data for three categorical variables. The contents and spatial distributions of Cu in stream sediment and rock samples across the boundary area of China and Mongolia were studied. The stream sediment geochemical data set was centered logratio transformed (clr) to avoid the closure effect. Random forest regression (RFR) was applied to predict Cu geochemical background for each sample with tectonic unit, geological background, and geomorphic landscape as characteristic variables, then the residuals were used to map geochemical anomalies. The study shows that Cu contents in stream sediments and rocks are high in the west and low in the east of the study area, and the median Cu varies with different tectonic units, geological backgrounds, and geomorphic landscapes. The background values of Cu are strongly spatial heterogeneous. The distributions of predicted Cu background values by random forest regression algorithm are similar to the distribution characteristics of Cu in stream sediments and rocks. The influence of tectonic units on the spatial variations of Cu geochemical background was greatest, followed by geological background, the influence of geomorphic landscape was smallest. The geochemical anomalies drawn by the residuals produced in random forest regression are in good agreement with the known deposits, indicating that the predicted geochemical background of Cu is reasonable and accurate, and has certain theoretical and practical significance.
Multi-layer path planning control for the simulation of manipulation tasks: Involving semantics and topology

The industrial and research communities show increasing interest in using automatic path planning techniques for the simulation of manipulation tasks. Automatic path planning, largely explored by the robotics community over the past 30 years, computes the trajectories of robots or manipulated parts. However, as techniques developed so far use mostly purely (and large) geometric models, they may fail, produce a trajectory of little relevance, or lead to very high computation times, when facing complex or very constrained environments. Involving higher abstraction level information should lead to better relevance of the simulation. In this paper, we propose a novel path planning technique relying on an original multi-layer environment model containing geometrical, topological and semantic layers. A first coarse planning step at the topological and semantic layers and a fine planning step at the local and semantically characterized geometrical layer form the path planning process. Experimental full-scale results show increased control on the planning process, leading to much lower computation times and increased relevance of the computed trajectory .
Bounding Uncertainty for Active Batch Selection

The success of batch mode active learning (BMAL) methods lies in selecting both representative and uncertain samples. Representative samples quickly capture the global structure of the whole dataset, while the uncertain ones refine the decision boundary. There are two principles, namely the direct approach and the screening approach, to make a trade-off between representativeness and uncertainty. Although widely used in literature, little is known about the relationship between these two principles. In this paper, we discover that the two approaches both have shortcomings in the initial stage of BMAL. To alleviate the shortcomings, we bound the certainty scores of unlabeled samples from below and directly combine this lower-bounded certainty with representativeness in the objective function. Additionally, we show that the two aforementioned approaches are mathematically equivalent to two special cases of our approach. To the best of our knowledge, this is the first work that tries to generalize the direct and screening approaches. The objective function is then solved by super-modularity optimization. Extensive experiments on fifteen datasets indicate that our method has significantly higher classification accuracy on testing data than the latest state-of-the-art BMAL methods, and also scales better even when the size of the unlabeled pool reaches 106.
Meta Learning for Image Captioning

Reinforcement learning (RL) has shown its advantages in image captioning by optimizing the non-differentiable metric directly in the reward learning process. However, due to the reward hacking problem in RL, maximizing reward may not lead to better quality of the caption, especially from the aspects of propositional content and distinctiveness. In this work, we propose to use a new learning method, meta learning, to utilize supervision from the ground truth whilst optimizing the reward function in RL. To improve the propositional content and the distinctiveness of the generated captions, the proposed model provides the global optimal solution by taking different gradient steps towards the supervision task and the reinforcement task, simultaneously. Experimental results on MS COCO validate the effectiveness of our approach when compared with the state-of-the-art methods.
Generalized Planning via Abstraction: Arbitrary Numbers of Objects

We consider a class of generalized planning problems based on the idea of quantifying over sets of similar objects. We show how we can adapt fully observable nondeterministic planning techniques to produce generalized solutions that are easy to instantiate over particular problem instances. We also describe how we can reformulate a classical planning problem into a quantified one. The reformulation allows us to solve the original planning task without grounding every action with respect to all objects in the problem, and a single solution can be applied to a possibly infinite set of related classical planning tasks. We report experimental results that show our approach is a practical and promising technique for solving an interesting class of problems.
Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation

Finding a person across a camera network plays an important role in video surveillance. For a real-world person re-identification application, in order to guarantee an optimal time response, it is crucial to find the balance between accuracy and speed. We analyse this trade-off, comparing a classical method, that comprises hand-crafted feature description and metric learning, in particular, LOMO and XQDA, to deep learning based techniques, using image classification networks, ResNet and MobileNets. Additionally, we propose and analyse network distillation as a learning strategy to reduce the computational cost of the deep learning approach at test time. We evaluate both methods on the Market-1501 and DukeMTMC-reID large-scale datasets, showing that distillation helps reducing the computational cost at inference time while even increasing the accuracy performance.
Exploring Classroom Emotion with Cloud-Based Facial Recognizer in the Chinese Beginning Class: A Preliminary Study

The Facial Emotion Recognizer (FER) detection technology in the education field is in the early stage.Objective, to investigate the classroom emotion and the effectiveness of the Microsoft cloud-based FER interpretations The randomly selected 29 international students who enrolled the fundamental Chinese language course were investigated during five study weeks using the paper-based Student Outcome Survey which measured teaching, assessment, generic skill and learning experience.For FER, the 784 students' facial images were extracted and uploaded into the cloud-based APIs.The students with GPA  80% showed higher significantly in Assessment components.No significant difference in FER emotion detection was found among five study weeks; however, natural and happiness emotion scores showed symmetric significantly in the opposite direction with r = -0.82,P < 0.1.The students' overall outcomes were over average.FER indicated that the neutral emotion was the highest detected score.The APIs Reliability was still considerably questioned.Using updated quality and technology of photo and video recording were recommended in the further research.
Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

More accurate data of hourly Global Horizontal Irradiance (GHI) are required in the field of solar energy in areas with limited ground measurements. The aim of the research was to obtain more precise and accurate hourly GHI by using new input from Satellite-Derived Datasets (SDDs) with new input combinations of clear sky (Cs) and top-of-atmosphere (TOA) irradiance on the horizontal surface and with observed climate variables, namely Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (WS). The variables were placed in ten different sets as models in an artificial neural network with the Levenberg–Marquardt training algorithm to obtain results from training, validation and test data. It was applied at two station types in northeast Iraq. The test data results with observed input variables (correlation coefficient (r) = 0.755, Root Mean Square Error (RMSE) = 33.7% and bias = 0.3%) are improved with new input combinations for all variables (r = 0.983, RMSE = 9.5% and bias = 0.0%) at four automatic stations. Similarly, they improved at five tower stations with no recorded SD (from: r = 0.601, RMSE = 41% and bias = 0.7% to: r = 0.976, RMSE = 11.2% and bias = 0.0%). The estimation of hourly GHI is slightly enhanced by using the new inputs.
A Lightweight Fine-Grained Search Scheme over Encrypted Data in Cloud-Assisted Wireless Body Area Networks

The wireless body area networks (WBANs) have emerged as a highly promising technology that allows patients’ demographics to be collected by tiny wearable and implantable sensors. These data can be used to analyze and diagnose to improve the healthcare quality of patients. However, security and privacy preserving of the collected data is a major challenge on resource-limited WBANs devices and the urgent need for fine-grained search and lightweight access. To resolve these issues, in this paper, we propose a lightweight fine-grained search over encrypted data in WBANs by employing ciphertext policy attribute based encryption and searchable encryption technologies, of which the proposed scheme can provide resource-constraint end users with fine-grained keyword search and lightweight access simultaneously. We also formally define its security and prove that it is secure against both chosen plaintext attack and chosen keyword attack. Finally, we make a performance evaluation to demonstrate that our scheme is much more efficient and practical than the other related schemes, which makes the scheme more suitable for the real-world applications.
Analysis of polarization fluctuation in long-distance aerial fiber for QKD system design

The polarization basis misalignment caused by polarization fluctuation is the main limiting factor in the deployment of polarization-encoding quantum key distribution (QKD) in aerial fibers. In this study, we conduct further research on polarization fluctuations in aerial fibers from the perspective of QKD. We propose a method to evaluate polarization fluctuation in aerial fibers. The relation connecting the quantum bit error rate (QBER) to the components of the Stokes vector is derived. Through a field experiment, long-time data on the polarization fluctuations in a 68-km-long aerial fiber are obtained. A statistical analysis of the time-cost corresponding to a typical QBER caused by polarization misalignment is conducted. The results can be used to evaluate the required speed of polarization compensation, and they indicate that the time-cost of the polarization compensation module should ideally be lower than 1 ms. This can provide guidance for the design of polarization compensation modules and algorithms for polarization-encoding QKD systems based on long-distance aerial fibers, aiding in its deployment.
Information Recommendation Based on Domain Knowledge in App Descriptions for Improving the Quality of Requirements

Apps have concentrated sale platforms, in which there often exist some products similar to the new App to be developed. The main features of these products are given in their introductions, providing an important resource for developers to improve the quality of the requirements of their own App. In this paper, we propose an approach to gain and recommend requirements related information from App descriptions to help developers use the resource efficiently. First, we construct a model by mining domain knowledge from App descriptions with the method proposed in our previous work and use initial requirements to retrieve their related information from the model. Then, we analyze the information and recommend them from three aspects: static information of the existing Apps for identifying the priorities of requirements; functional features and non-functional properties of features for giving the detailed design of the Apps in requirements; and the combinations of features for enriching the requirements. To validate the proposed approach, we conducted experiments and a survey based on the data in Google Play. The results show that our approach can identify the existing products with initial requirements reasonably, and also indicate that the developers confirm the usefulness of the recommended information in practice.
Blind modulation format identification using the DBSCAN algorithm for continuous-variable quantum key distribution

Advances in adaptive modulation techniques have fueled the growth of classic communication recently, and the modulation format identification (MFI) has been extensively studied in the field of wireless communication, but in order to make Alice and Bob smoothly enter post-processing and develop toward an adaptive network, the MFI concept is worth reviewing for a continuous-variable quantum key distribution (CV-QKD) system. This paper proposes a constellation MFI scheme based on the density-based spatial clustering of applications with noise (DBSCAN) machine learning algorithm, and the MFI process is set at the receiving end of the CV-QKD system. The proposed MFI scheme for 4-QAM, 16-QAM, 64-QAM, and 256-QAM signals can reach high accuracy (&gt;99%) when the signal-to-noise ratio (SNR) is greater than or equal to 23 dB. The simulation results show that using the DBSCAN unsupervised machine learning algorithm can achieve good identification performance. The proposed MFI scheme can be further improved at lower SNR by optimizing the algorithm and increasing the number of samples.
A new design in iterative image deblurring for improved robustness and performance

In many applications, image deblurring is a pre-requisite to improve the sharpness of an image before it can be further processed. Iterative methods are widely used for deblurring images but care must be taken to ensure that the iterative process is robust, meaning that the process does not diverge and reaches the solution reasonably fast, two goals that sometimes compete against each other. In practice, it remains challenging to choose parameters for the iterative process to be robust. We propose a new approach consisting of relaxed initialization and pixel-wise updates of the step size for iterative methods to achieve robustness. The first novel design of the approach is to modify the initialization of existing iterative methods to stop a noise term from being propagated throughout the iterative process. The second novel design is the introduction of a vectorized step size that is adaptively determined through the iteration to achieve higher stability and accuracy in the whole iterative process. The vectorized step size aims to update each pixel of an image individually, instead of updating all the pixels by the same factor. In this work, we implemented the above designs based on the Landweber method to test and demonstrate the new approach. Test results showed that the new approach can deblur images from noisy observations and achieve a low mean squared error with a more robust performance.
Towards Low-Latency Batched Stream Processing by Pre-Scheduling

Many stream processing frameworks have been developed to meet the requirements of real-time processing. Among them, batched stream processing frameworks are widely advocated with the consideration of their fault-tolerance, high throughput and unified runtime with batch processing. In batched stream processing frameworks, straggler, happened due to the uneven task execution time, has been regarded as a major hurdle of latency-sensitive applications. Existing straggler mitigation techniques, operating in either reactive or proactive manner, are all post-scheduling methods, and therefore inevitably result in high resource overhead or long job completion time. We notice that batched stream processing jobs are usually recurring with predictable characteristics. By exploring such a heuristic, we present a pre-scheduling straggler mitigation framework called Lever. Lever first identifies potential stragglers and evaluates nodes’ capacity by analyzing execution information of historical jobs. Then, Lever carefully pre-schedules job input data to each node before task scheduling so as to mitigate potential stragglers. We implement Lever and contribute it as an extension of Apache Spark Streaming. Our experimental results show that Lever can reduce job completion time by 30.72 to 42.19 percent over Spark Streaming, a widely adopted batched stream processing system and outperforms traditional techniques significantly.
MOEA/D with the online agglomerative clustering based self-adaptive mating restriction strategy

In MOEA/D-DE, the appropriate value of the mating restriction probability varies with the evolutionary process. Furthermore, different subproblems have been solved in different degree during the evolution, so different subproblems have distinct requirements for exploitation and exploration. Additionally, MOEA/D-DE defines the neighborhood according to the distance between the weight vectors. However, the individuals corresponding to the neighbor subproblems may distribute far away in the decision space, which will affect the performance of exploitation. Accordingly, this paper proposes a MOEA/D with the online agglomerative clustering based self-adaptive mating restriction strategy (MOEA/D-OMR). MOEA/D-OMR utilizes the online agglomerative clustering algorithm to extract the neighborhood information in the decision space. The mating pool is then constructed by the neighbor population or the whole population based on the mating restriction probability. What is more, a separate mating restriction probability is assigned to each subproblem. The mating restriction probability is updated at each generation by the survival length, which is the number of generations that the solution has survived over the last certain period of time. Experimental results show that MOEA/D-OMR has a better performance than the comparison algorithms.
Articulatory and Spectrum Information Fusion Based on Deep Recurrent Neural Networks

Many studies have shown that articulatory features can significantly improve the performance of automatic speech recognition systems. Unfortunately, such features are not available at recognition time. There are two main approaches to solve this problem: a feature-based approach, the most popular example of which is the acoustic-to-articulatory inversion, where the missing articulatory features are generated from the speech signal, and a model-based approach, where articulatory information is embedded in the model structure and parameters in a way that allows recognition using only acoustic features. In this paper, we propose two new methods to integrate articulatory information into a phoneme recognition system. One of them is feature based, and the other is model based. In both cases, the underlying acoustic model (AM) is a deep neural networks-hidden Markov model (DNN-HMM) hybrid. In the feature-based method, the articulatory inversion DNN and the acoustic model DNN are trained jointly using a linear combination of their loss functions. In the model-based method, we utilize the generalized distillation framework to train the AM DNN. In this case, first, a teacher DNN is trained on both the acoustic and articulatory features, and then its outputs are used as additional targets during the AM DNN training with acoustic features only. A 7-fold cross-validation experiments using 42 speakers from the XRMB database showed that both the proposed methods provide about 22% to 25% performance improvement with respect to the DNN acoustic model trained with acoustic features only.
Digital immunohistochemistry implementation, training and validation: experience and technical notes from a large clinical laboratory

Aims To consider the value proposition of digitisation of clinical immunohistochemistry services, and to develop an approach to digital immunohistochemistry implementation and validation in a large clinical laboratory. Methods A methodology for slide scanning in the laboratory was developed, in addition to a novel validation exercise, to allow pathologists to identify the strengths and weaknesses of digital immunohistochemistry reporting, and train in digital immunohistochemistry slide assessment. Results A total of 1480 digital immunohistochemistry slides were assessed by 24 consultant pathologists, with complete clinical concordance between the digital and the glass slide assessment observed. Certain stains were identified as being difficult/time consuming to assess using ×20 digital slides. These stains were rescanned at ×40, which improved the confidence of the pathologists to make a digital assessment. Full digitisation of immunohistochemistry slides was achieved, introducing six new steps into the pre-existing laboratory workflow. Conclusions While initially encountering challenges in terms of workflow, our experience showed that a well-designed, adequately resourced and well-managed scanning process can minimise the delay in slides being made available for review. Our approach to validation highlighted the need for careful assessment of a digital pathology system and scanning protocols before pathologists are expected to transfer from the light microscope to the digital microscope for routine immunohistochemistry assessment.