Reviewerly/exHarmony huggingface dataset:
Research and Implementation of QA System Based on the Knowledge Graph of Chinese Classic Poetry

With the rapid development of the Internet, intelligent QA (Question Answering) system has been widely used in telecom operators, financial services, e-commerce shopping and other industries, but there are few researches and applications of intelligent QA system in the field of Chinese classical poetry. In view of the above situation, this paper aims to implement an automatic QA system based on the knowledge graph of Chinese classical poetry by combining natural language processing technology. In terms of the construction of knowledge graph, the common triads of Chinese classical poetry knowledge was extracted from the classical poetry websites and the knowledge graph of Chinese classical poetry stored with Neo4j was constructed. In the aspect of question recognition and multi-round dialogue, the Rasa framework was adopted to extract the entity and identify the intention of the user's questions in Chinese classical poetry, so as to realize multi-round dialogue.
Image Inpainting for Object Removal Based on Adaptive Two-Round Search Strategy

In traditional inpainting method for object removal, the SSD (Sum of Squared Differences) is always used to measure the degree of similarity between exemplar patch and target patch. Although the matching rule is simple, there is a risk that the target patch is replaced by an unsuitable exemplar patch, which leads to the mismatch error. Even worse, the error may be constantly accumulated along with the process progresses, finally some unexpected objects will be introduced into target region, and the restored image cannot meet the requirements of human vision. In view of these problems, we propose an inpainting method based on adaptive two-round search strategy. Firstly, we define the DBP (Differences Between Patches) between target patch and exemplar patch, and use it to measure the degree of difference between the two patches. Then, based on SSD and DBP, we adaptively judge whether there is a mismatch error. If the mismatch error occurs, the two-round search strategy is implemented. We define a new matching rule and use it to re-search the exemplar patch. Finally, we use the exemplar patch to restore the target patch. Experimental results demonstrate the effectiveness of our method. It can effectively prevent the occurrence of mismatch error and error accumulation, improve the restoration effect.
Joint Pricing and Security Investment in Cloud Security Service Market With User Interdependency

After several decades of development on cyber security techniques, one clear conclusion can be drawn: no cyber security solution can completely remove the risks faced by the users. In this regard, cyber-insurance has been introduced as a means to enable the users to alleviate the damage from the cyber threats by transferring the cyber risks to an insurer. In this article, we study a cloud security service market, which is composed of cloud users and cloud security service vendors (CSSVs). The CSSVs work as the insurers for selling the cloud security plan, which is consisted of cloud security service and cloud-insurance. The users in the cloud platform can purchase the cloud security plan from the CSSVs to secure their cloud service. If the cloud service is attacked and loss happens, the users will receive the claim from the CSSVs. To lower the successful attack probability, the CSSV has an incentive to invest in improving its cloud security service. Specifically, we model and study the cloud security service market in the framework of a two-stage Stackelberg game. On the upper stage, the CSSVs lead to decide on their own strategies, i.e., the price of the cloud security plan and the security investment to improve their offered cloud security service. On the lower stage, the users follow to decide on the purchase of the cloud security plan according to the price of the cloud security plan and the perceived cyber breach probability of the cloud security service. We analytically verify that the Stackelberg equilibrium exists and is unique. Extensive simulations have been conducted to evaluate the performance of the Stackelberg game. The performance evaluation shows some insightful results. For example, when the users have strong interdependency, the profits of the CSSVs become lower.
Group Feedback Capsule Network

In capsule networks (CapsNets), the capsule is made up of collections of neurons. Their adjacent capsule layers are connected using routing-by-agreement mechanisms in an unsupervised way. The routing-by-agreement mechanisms have two main drawbacks: a) too many parameters and high computation complexity; b) the cluster distribution assumptions of these routing mechanisms may not hold in some complex real-world data. In this paper, we propose a novel Group Feedback Capsule Network (GF-CapsNet) which adopts a supervised routing strategy called group-routing. Compared with the previous routing strategies which globally transform each capsule, Group-routing equally splits capsules into groups where capsules locally share the same transformation weights, reducing routing parameters. To address the second drawback, we devise a distance network to directly predict capsules in a supervised way without making distribution assumptions. Our proposed group-routing captures local information of low-level capsules by group-wise transformation and supervisedly predicts high-level ones in a feedback way to address two drawbacks respectively. We conduct experiments on CIFAR-10/100 and SVHN datasets and the results show that our method can perform better against state-of-the-arts.
Escape The Countries: A VR Escape Room Game

Technology has become an integral part of our life. One of the most emerging technologies of the 21st century is Virtual Reality. It is being applied to medicine, education, engineering, architecture, training, entertainment and so on. Nowadays it is being used in the gaming field as well. Game developers are turning their games into VR games and it is gaining a lot of popularity. Virtual reality gaming is the application of a 3D artificial environment to games. A VR gaming accessory involves VR headsets, sensor-equipped gloves, hand controllers, etc. Virtual reality games can be played on standalone systems, on specialized game consoles, or using advanced laptops and PCs using Oculus Rift, HTC Vive and Lenovo Explorer [1]. An escape room game is a game where players have to `escape' a room by solving challenges within a given time limit [2]. Sometimes the challenges are made inaccessible and must be found by completing puzzles. Therefore, this project proposes to create a VR escape room game that will help the players to enhance their skills. The game will have several rooms to escape representing different cultures of different countries to help the player interact with different cultures. The game will have puzzle-solving challenges to solve so that players can escape the room. The challenges will be based on math, arrangement, and pattern recognition. During the project, a survey will be carried out to gather information about playing VR games and the significance of a VR escape room game. Similar games around the world will also be reviewed. The goal of the project is to use people's free time to teach them skills.
Nonasymptotic Gaussian Approximation for Inference With Stable Noise

The results of a series of theoretical studies are reported, examining the convergence rate for different approximate representations of α-stable distributions. Although they play a key role in modelling random processes with jumps and discontinuities, the use of α-stable distributions in inference often leads to analytically intractable problems. The LePage series, which is a probabilistic representation employed in this work, is used to transform an intractable, infinite-dimensional inference problem into a finite-dimensional (conditionally Gaussian) parametric problem. A major component of our approach is the approximation of the tail of this series by a Gaussian random variable. Standard statistical techniques, such as ExpectationMaximization (EM), Markov chain Monte Carlo, and Particle Filtering, can then be readily applied. In addition to the asymptotic normality of the tail of this series, we establish explicit, nonasymptotic bounds on the approximation error. Their proofs follow classical Fourier-analytic arguments, using Esséen's smoothing lemma. Specifically, we consider the distance between the distributions of: (i) the tail of the series and an appropriate Gaussian; (ii) the full series and the truncated series; and (iii) the full series and the truncated series with an added Gaussian term. In all three cases, sharp bounds are established, and the theoretical results are compared with the actual distances (computed numerically) in specific examples of symmetric αstable distributions. This analysis facilitates the selection of appropriate truncations in practice and offers theoretical guarantees for the accuracy of resulting estimates. One of the main conclusions obtained is that, for the purposes of inference, the use of a truncated series together with an approximately Gaussian error term has superior statistical properties and is likely a preferable choice in practice.
Human Age Prediction from Facial Image Using Transfer Learning in Deep Convolutional Neural Networks

Automatic age estimation task has attracted attention due to its numerous applications particularly in the field of social media and e-commerce. In this study, a pipeline method of age estimation from human facial images has been investigated where different pre-trained deep convolutional neural networks (DCNNs) are managed through transfer learning. Age may be represented by an integer or floating-point number, but it has some coherence; facial images of few consecutive years of an individual are not so different; even human eye could differentiate a little. Therefore, age estimation of the present study is performed as both regression and classification tasks to show which method preserves more coherence. Only additional layer(s) to the pre-trained DCNNs are reformed for this purpose. Different year groupings (individual, five and ten) are also considered in case of classification. In the proposed method, different DCNN versions of ResNets, Inception and DenseNet are considered on cross-age celebrity dataset (CACD), UTKFace and FGNet datasets. The proposed method is shown to achieve remarkable performance while compared with the existing methods.
A new method of Bayesian causal inference in non-stationary environments

Bayesian inference is the process of narrowing down the hypotheses (causes) to the one that best explains the observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method such as exponential moving average (EMA) with a discounting rate is used to improve the ability to respond to a sudden change; it is also necessary to increase the discounting rate. That is, a trade-off is established in which the followability is improved by increasing the discounting rate, but the accuracy is reduced. Here, we propose an extended Bayesian inference (EBI), wherein human-like causal inference is incorporated. We show that both the learning and forgetting effects are introduced into Bayesian inference by incorporating the causal inference. We evaluate the estimation performance of the EBI through the learning task of a dynamically changing Gaussian mixture model. In the evaluation, the EBI performance is compared with those of the EMA and a sequential discounting expectation-maximization algorithm. The EBI was shown to modify the trade-off observed in the EMA.
Pointer gauge adaptive reading method based on a double match

In large-scale power transmission stations, more and more automated pointer gauge reading algorithms are used to automatically and efficiently take gauge readings. However, most existing pointer gauge reading algorithms did not take into account image deformation, the changes of illumination, and small objects interference in one algorithm, so these algorithms have poor robustness and adaptability to real environments with complex situations, and cannot obtain correct gauge readings steadily. In this paper, we present a pointer gauge adaptive reading method based on Double-Match. Our Double-Match gauge reading method is dedicated to reading the gauge adaptively with some strong anti-interference methods after the image registration. To achieve this, various gauge images should be registered first by perspective transform based on feature points detection and matching, and then perform pointer center positioning and gauge reading calculations based on multiple image moments matching. In the experimental part, comparing with the classical algorithm based on the saliency and Hough line detection in the field of gauge reading, our method shows better precision and better anti-interference, furthermore the adaption of the pointer gauge reading algorithm is effectively improved.
A New Framework Combining Local-Region Division and Feature Selection for Micro-Expressions Recognition

Micro-expressions are deliberate or unconscious movements of people's psychological activities, reflecting the transient facial true expressions. Previous works focus on the whole face for micro-expressions recognition. These methods can extract a number of feature vectors which are relevant or irrelevant to the micro-expressions recognition. Besides, the high-dimension feature vectors can result in longer computational time and increased computational complexity. In order to address these problems, we propose a new framework which combines the local-region division and the feature selection. Based on the proposed framework, the original images can retain more efficient regions and filter out the invalid components of feature vectors. Specifically, with the joint efforts of the facial deformation identification model and facial action coding system, the global region is divided into seven local regions with their corresponding actions units. The ReliefF algorithm is used to select effective components of feature vectors and reduce the dimension. To evaluate the proposed framework, we conduct experiments on both the Chinese Academy of Sciences Micro-expression II Database and Spontaneous Micro-expression Database with Leave-One-Subject-Out Cross Validation method. The results show that the performance in local combined regions outperforms its counterpart in the global region, and the recognition accuracy is further improved with the combination of feature selection.
Facial Expression Recognition with Active Local Shape Pattern and Learned-Size Block Representations

Facial expression recognition has been studied broadly, and several works using local micro-pattern descriptors have obtained significant results. There are, however, open questions: how to design a discriminative and robust feature descriptor?, how to select expression-related most influential features?, and how to represent the face descriptor exploiting the most salient parts of the face? In this article, we address these three issues to achieve better performance in recognizing facial expressions. First, we propose a new feature descriptor, namely Local Shape Pattern (LSP), that describes the local shape structure of a pixel’s neighborhood based on the prominent directional information by analyzing the statistics of the neighborhood gradient, which allows it to be robust against subtle local noise and distortion. Furthermore, we propose a selection strategy for learning the influential codes being active in the expression affiliated changes by selecting them exhibiting statistical dominance and high spatial variance. Lastly, we learn the size of the salient facial blocks to represent the facial description with the notion that changes in expressions vary in size and location. We conduct person-independent experiments in existing datasets after combining above three proposals, and obtain an improved performance for the facial expression recognition task.
Exploration of the Application of Virtual Reality and Internet of Things in Film and Television Production Mode

In order to reduce some of the problems of technological restructuring and insufficient expansion in the current film and television production mode, the application of emerging technologies such as artificial intelligence (AI), virtual reality (VR), and Internet of Things (IoT) in the film and television industry is introduced in this research. First, a topical crawler tool was constructed to grab relevant texts about “AI”, “VR”, and “IoT” crossover “film and television”, and the grasping accuracy rate and recall rate of this tool were compared. Then, based on the extracted text, the data of recent development in related fields were extracted. The AdaBoost algorithm was used to improve the BP (Back Propagation) neural network (BPNN). This model was used to predict the future development scale of related fields. Finally, a virtual character interaction system based on IoT-sensor technology was built and its performance was tested. The results showed that the topical crawler tool constructed in this study had higher recall rate and accuracy than other tools, and a total of 188 texts related to AI, VR, and IoT crossover television films were selected after Naive Bayes classification. In addition, the error of the BPNN prediction model based on the AdaBoost algorithm was less than 20%, and it can effectively predict the future development scale of AI and other fields. In addition, the virtual character interaction system based on IoT technology constructed in this study has a high motion recognition rate, produces a strong sense of immersion among users, and can realize real-time capture and imitation of character movements. In a word, the field of AI and VR crossover film and television has great development prospects in the future. Therefore, the application of IoT technology in building the virtual-character interaction system can improve the effect of VR or AI film and television production.
CompliancePal: A Tool for Supporting Practical Agile and Regulatory-Compliant Development of Medical Software

As digital transformation affects more and more industries, the increased role of software and the skills required to develop software trigger a ripple effect. Entire industries, where regulations and government standards play an important role (e.g. health care, avionics, etc.), have used long development cycles that relied on detailed up-front planning before advancing to any detailed decision. In contrast to this mindset, agile software development has proven to deliver results that satisfy customers needs faster than traditional waterfall methodologies. The lack of detailed upfront planning and fast delivery cycles have led to situations where the use of agile became synonymous with lack of documentation and poor quality, and hence the perception that the approach is not suitable for regulated systems. In this experience paper we describe the implementation of a service that integrates medical device software compliance specific activities such as architectural design and limited risk management into the daily agile practices of a software development team.
Serendipity and me

Abstract How much of your scientific career has unfolded as you planned, and how much has been shaped by blind luck? I suspect the latter has been more important than many of us realize, or at least acknowledge, but as Louis Pasteur said, “Chance favors only the prepared mind”—implying that we have at least some control over how random events affect our lives. Here, I discuss how large and small chance events have affected the trajectory of my scientific career.
Medical virtual reality

“Meet me for dinner on the Holodeck” is a familiar scenario from the television series Star Trek. It is a fictional space where participants engage with different virtual reality (VR) environments and allows for interaction with a variety of locations and characters that might not otherwise be possible. The Holodeck has an advantage of being a safer alternative to reality where there is an experience between human and computer in a simulated and immersive environment. Record your answers on the Return Answer Form found on the tear-out coupon at the back of this issue or to complete online and use a credit card, go to JHTReadforCredit.com. There is only one best answer for each question.# 1.A common tactic using VR in a medical setting is calleda.substitution and additionb.deception or deceitc.distraction or diversiond.conversion and completion# 2.An example of a non-medical device being used as a VR tool is thea.Haptic gloveb.Holistic glovec.Harvard gloved.Homerun baseball glove# 3.One hand motion tracking device is thea.Michigan Molded exoskeletonb.Miami Model Machinec.Minnesota Multi-motion Scannerd.Manus Machina INC# 4.One potential drawback for the use of VR glove devices in hand rehabilitation is the glovea.is often too painful to applyb.may be esthetically rejected by the patientc.may limit motiond.is usually not covered by third party payers# 5.Though much of VR involves video gaming which appeals to a young population raised in a world laden with computers, due to the immersive nature of these technologies, older populations are likely to adopt to such applicationsa.not trueb.true When submitting to the HTCC for re-certification, please batch your JHT RFC certificates in groups of 3 or more to get full credit.
Entropic Dynamics in Neural Networks, the Renormalization Group and the Hamilton-Jacobi-Bellman Equation

We study the dynamics of information processing in the continuum depth limit of deep feed-forward Neural Networks (NN) and find that it can be described in language similar to the Renormalization Group (RG). The association of concepts to patterns by a NN is analogous to the identification of the few variables that characterize the thermodynamic state obtained by the RG from microstates. To see this, we encode the information about the weights of a NN in a Maxent family of distributions. The location hyper-parameters represent the weights estimates. Bayesian learning of a new example determine new constraints on the generators of the family, yielding a new probability distribution which can be seen as an entropic dynamics of learning, yielding a learning dynamics where the hyper-parameters change along the gradient of the evidence. For a feed-forward architecture the evidence can be written recursively from the evidence up to the previous layer convoluted with an aggregation kernel. The continuum limit leads to a diffusion-like PDE analogous to Wilson's RG but with an aggregation kernel that depends on the weights of the NN, different from those that integrate out ultraviolet degrees of freedom. This can be recast in the language of dynamical programming with an associated Hamilton-Jacobi-Bellman equation for the evidence, where the control is the set of weights of the neural network.