Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Children's Games Based on Mixed Reality

Children's games are an important part of human growth. Vivid and educational children's games are more popular. In this paper, we design and develop a children's game system based on mixed reality technology by using a variety of digital media technologies. The system uses HoloLens combined with Unity as the main platform, taking animal cognition as an example for infant games design. It mainly includes vivid augmented reality animal display, virtual book about animal introduction, cognition of animal habits, interactive guidance and review consolidation. Through innovative and novel display forms, children can recognize animal related knowledge in a mixed reality environment. We are trying to explore new forms of realization for children's games.
Chinese Text Error Correction Suggestion Generation Based on SoundShape Code

Text error correction is an essential part of text proofreading. This paper presents a method for generating text error correction suggestion based on SoundShape Code. By converting the target words into SoundShape Code and using an improved editing distance algorithm to make an ambiguous match with the words in the vocabulary, a set of candidate words whose similarity exceeds a certain threshold are obtained. Based on the contextual relevance model, each words in the candidate words set is scored, and then reasonable error correction suggestions are given according the score. In this paper, four types of errors are marked: substitution error in words with two-character, missing error in words with more than three-character, inserting error in words with more than three-character, substitution error in words with three-character. In total, 617 errors are tested and analyzed. Experiments show that the similarity calculation based on SoundShape Code can provide reasonable error correction suggestions.
Network as Regularization for Training Deep Neural Networks: Framework, Model and Performance

Despite powerful representation ability, deep neural networks (DNNs) are prone to over-fitting, because of over-parametrization. Existing works have explored various regularization techniques to tackle the over-fitting problem. Some of them employed soft targets rather than one-hot labels to guide network training (e.g. label smoothing in classification tasks), which are called target-based regularization approaches in this paper. To alleviate the over-fitting problem, here we propose a new and general regularization framework that introduces an auxiliary network to dynamically incorporate guided semantic disturbance to the labels. We call it Network as Regularization (NaR in short). During training, the disturbance is constructed by a convex combination of the predictions of the target network and the auxiliary network. These two networks are initialized separately. And the auxiliary network is trained independently from the target network, while providing instance-level and class-level semantic information to the latter progressively. We conduct extensive experiments to validate the effectiveness of the proposed method. Experimental results show that NaR outperforms many state-of-the-art target-based regularization methods, and other regularization approaches (e.g. mixup) can also benefit from combining with NaR.
Object Detection in X-ray Images Using Transfer Learning with Data Augmentation

Object detection in X-ray images is an interesting problem in the field of machine vision. The reason is that images from an X-ray machine are usually obstructed with other objects and to itself, therefore object classification and localization is a challenging task. Furthermore, obtaining X-ray data is difficult due to an insufficient dataset available compared with photographic images from a digital camera. It is vital to easily detect objects in an X-ray image because it can be used as decision support in the detection of threat items such as improvised explosive devices (IED’s) in airports, train stations, and public places. Detection of IED components accurately requires an expert and can be achieved through extensive training. Also, manual inspection is tedious, and the probability of missed detection increases due to several pieces of baggage are scanned in a short period of time. As a solution, this paper used different object detection techniques (Faster R-CNN, SSD, R-FCN) and feature extractors (ResNet, MobileNet, Inception, Inception-ResNet) based on convolutional neural networks (CNN) in a novel IEDXray dataset in the detection of IED components. The IEDXray dataset is an X-ray image of IED replicas without the explosive material. Transfer learning with data augmentation was performed due to limited X-ray data available to train the whole network from scratch. Evaluation results showed that individual detection achieved 99.08% average precision (AP) in mortar detection and 77.29% mAP in three IED components.
Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning

Multi-task learning (MTL) is a common paradigm that seeks to improve the generalization performance of task learning by training related tasks simultaneously. However, it is still a challenging problem to search the flexible and accurate architecture that can be shared among multiple tasks. In this paper, we propose a novel deep learning model called Task Adaptive Activation Network (TAAN) that can automatically learn the optimal network architecture for MTL. The main principle of TAAN is to derive flexible activation functions for different tasks from the data with other parameters of the network fully shared. We further propose two functional regularization methods that improve the MTL performance of TAAN. The improved performance of both TAAN and the regularization methods is demonstrated by comprehensive experiments.
Algorithms for Anoraks

As the 2019 Cricket World Cup builds up to its finale in July, it seems that techno-nerds are fast becoming the game's new anoraks.
Words that Hurt (2): National and International Perspectives on Hate Speech Regulation

Faced with a piecemeal approach to hate speech in Europe, leading to the reduced visibility of the phenomenon with often serious consequences, a variety of regional and international organisations have contributed legal documents and interpretative recommendations that attempt to guide states in their practice of combating hate speech.The present paper, following up on a previous one, will engage first with the international legal and regulatory framework of hate speech, placing emphasis on the European elements of the system in place. At a second stage, the paper will briefly survey twenty European national systems exposing the variety of regulatory patterns on the issue. Finally, the study will conclude with a list of common observations pertaining to the regulation of hate speech in the European continent, as they have emerged from the comparative analysis of the case-studies.
Seismic Sensors in Orbit

Navigation satellites are enabling high-precision, real-time tracking of ground displacements, supplementing traditional methods for monitoring and assessing earthquakes.
A Performance Analysis and Evaluation of SIDH with Implementation-Friendly Classes of Quadratic Extension Fields

It is well-known that the class of binomial extension fields is widely used to construct quadratic extension fields (QEFs) of supersingular isogeny Diffie-Hellman (SIDH) key exchange protocol. There is a possibility to improve the performance of SIDH by employing other classes of QEFs, i.e., extension fields with normal basis and all-one polynomial extension fields, without sacrificing the range of primes. In this paper, the authors confirm that the applicability of the other classes for SIDH and evaluate the computational complexity of the large-degree isogenies required for SIDH. The results of the experiments show that the performances with the classes are comparable to the QEF with a binomial x^2+1.
Practical Applications of Cryptocurrency Survival Analysis

Practical Applications Summary In Cryptocurrency Survival Analysis, from the Winter 2020 issue of The Journal of Alternative Investments, Jan Lansky (University of Finance and Administration) investigates the probabilities of individual cryptocurrencies being delisted. Although exchanges list more than 2,500 cryptocurrencies, a very high percentage of them ultimately will be delisted. Lansky provides background on cryptocurrencies, describes the data collection procedure, tallies live and dead cryptocurrencies, and shows how to calculate conditional probabilities of their demise. Most cryptocurrencies fail within five years of being listed on an exchange, yet the conditional probability of survival increases with the life of the cryptocurrency. For example, the probability of being delisted within one year is 35% for a new cryptocurrency, 27% after trading for one year, 19% after trading for two years, and 12% after three years. TOPICS:Currency, statistical methods, risk management, exchanges/markets/clearinghouses
Merging Live and Static 360 Panoramas Inside a 3D Scene for Mixed Reality Remote Collaboration

We propose a new technique for Mixed Reality (MR) remote collaboration that uses live and static 360 panoramas inside a 3D scene. Prior work on MR remote collaboration demonstrated benefits of using a 360 panorama, or using a 3D scene. However, collaboration using 360 panorama offers limited spatial information. Our prototype system allows viewing and manipulating of 360 panoramas and visual cues inside a 3D scene, adding spatial information for enhanced contextual understanding between the remote collaborators. We designed a system that combines Augmented Reality (AR) and Virtual Reality (VR) to bridge the visual cues and information relative to the 3D space to help enhance the user experience and interaction in a larger scale MR remote collaboration. We describe our implementation in detail with a discussion of the design implications and potential benefits that could help improve MR remote collaboration.
Cross-Domain Deep Collaborative Filtering for Recommendation

Collaborative filtering (CF) faces two challenges for recommendations: data sparsity and cold-start issue. One solution is to incorporate the side information and the other is to utilize relevant knowledge. In this paper, a cross-domain deep collaborative filtering (CDDCF) model is proposed by considering both, which combines matrix tri-factorization and deep structure in both source and target domains. Deep structure takes as input the side information to learn latent representation. Matrix tri-factorization generates private latent factors connecting deep structure and common latent factors bridging relevant domains. Experiments on real datasets demonstrate its effectiveness.
Improving Convergence in IRGAN with PPO

Information retrieval modeling aims to optimise generative and discriminative retrieval strategies, where, generative retrieval focuses on predicting query-specific relevant documents and discriminative retrieval tries to predict relevancy given a query-document pair. IRGAN unifies the generative and discriminative retrieval approaches through a minimax game. However, training IRGAN is unstable and varies largely with the random initialization of parameters. In this work, we propose improvements to IRGAN training through a novel optimization objective based on proximal policy optimisation and gumbel-softmax based sampling for the generator, along with a modified training algorithm which performs the gradient update on both the models simultaneously for each training iteration. We benchmark our proposed approach against IRGAN on three different information retrieval tasks and present empirical evidence of improved convergence.
Research on Motion Path Planning of Industrial Robot Based on Genetic Algorithms

Based on the analysis and comparison of the advantages and disadvantages of various current path planning algorithms for industrial robot, this paper studies the path planning of industrial robots based on genetic algorithm. The algorithm uses real-coded variable-length chromosomes. The designed fitness function not only reflects the requirement of path, but also reduces the complexity of function representation and computation time. According to the characteristics of path planning, corresponding genetic operators are designed, and the influence of each parameter on the performance of the algorithm is analyzed. In the perceptible range of the robot, the position information of the motion obstacle is continuously obtained, the direction of motion is predicted, the search process is accelerated, and the feasible satisfactory path is obtained.
Automatic Classification of Civil Complaint Data Using CNN and Bidirectional LSTM ：The Case of Busan, South Korea

온라인과 정보통신기술의 발달로 정부정책에 대한 시민의 참여 욕구는 높아지고 있다. 이에 따라 시민들은 민원을 인터넷과 모바일을 활용하여 전자 민원 게시판을 통해 접수하는 건수가 증가하고 있다. 폭발적으로 늘어나는 민원의 양에 비해 아직 수작업으로 분류 하여 오류가 발생하거나 신속한 대응이 이루어지지 않아 민원인들의 불만이 늘어나고 있다. 본 연구에서는 딥러닝 기법을 통해 담당 부서 분류를 자동화하기 위해 2017년도의 부산시 민원 데이터를 수집하고, 담당 부서를 확인 할 수 있는 부서명, 전화번호, 담당자명을 기준으로 레이블을 부여하였다. 그리고 딥러닝 중 대표적인 분류방법인 CNN과 최근 여러 분야에서 두각을 내고 있는 Bidirectional LSTM을 기반으로 상위 12개 범주에 대하여 지도학습을 실시하였다. 지도학습 결과 각각 73%, 77%의 정확도를 보여 안정적인 성능을 보여주었다. 본 연구의 민원 분류에 대한 지도학습 사례는 향후 다른 주제 및 지방자치단체 민원에 대한 텍스트 데이터의 분류에 이용될 수 있어 실무적인 공헌도와 함께 후속연구를 유발할 수 있다는 학문적 기여도가 있다.The development of the Internet and information and communication technology fueled the desire of citizens to participate in government policy making. An increasingly number of individuals are registering civil complaints in electronic bulletin boards using the Internet and mobile devices. Despite the rapidly burgeoning volume of civil grievances posted online, however, such data are currently handled via manual methods, thereby causing frequent errors and delaying responses. These deficiencies ultimately exacerbate user dissatisfaction. In consideration of these matters, this study implemented an automatic classification of complaints issued by residents of Busan City, South Korea. The collected data were labeled in terms of the departments featured in the complaints, their telephone numbers, and the names of officers in charge. This information could be used to verify the department concerned in the complaints. Supervised learning was implemented on 12 upper complaint categories using a convolutional neural network, which is a representative deep learning approach, and bidirectional long short-term memory, which exhibits superior performance in diverse fields. The supervised learning results indicated that the two methods achieved classification accuracy levels of 73% and 77%, respectively. This study makes practical contributions to supervised learning that involves the classification of civil complaints as it can be employed in similar initiatives by other local governments or in the categorization of text data on a specific topic. The research adds value to the academic domain by opening up directions for further exploration in relevant fields.
DGS-HSA: A Dummy Generation Scheme Adopting Hierarchical Structure of the Address

With the increasing convenience of location-based services (LBSs), there have been growing concerns about the risk of privacy leakage. We show that existing techniques fail to defend against a statistical attack meant to infer the user’s location privacy and query privacy, which is due to continuous queries that the same user sends in the same location in a short time, causing the user’s real location to appear consecutively more than once and the query content to be the same or similar in the neighboring query. They also fail to consider the hierarchical structure of the address, so locations in an anonymous group may be located in the same organization, resulting in leaking of the user’s organization information and reducing the privacy protection effect. This paper presents a dummy generation scheme, considering the hierarchical structure of the address (DGS-HSA). In our scheme, we introduce a novel meshing method, which divides the historical location dataset according to the administrative region division. We also choose dummies from the historical location dataset with the two-level grid structure to realize the protection of the user’s location, organization information, and query privacy. Moreover, we prove the feasibility of the presented scheme by solving the multi-objective optimization problem and give the user’s privacy protection parameters recommendation settings, which balance the privacy protection level and system overhead. Finally, we evaluate the effectiveness and the correctness of the DGS-HSA through theoretical analysis and extensive simulations.
Deep Learning Techniques to Classify and Analyze Medical Imaging Data

The use of deep learning techniques predominantly the Convolutional Neural Networks (CNNs) has been used in various disciplines in recent years. CNNs have shown an essential ability to automatically extract large volumes of information from big data. The use of CNNs has significantly proved to be useful especially in classifying natural images. Nonetheless, there has been a major barrier in implementing CNNs in the medical domain due to lack of proper available data for training. As a result, generic imaging standards like ImageNet have been widely used in the medical domain. However, these generic imaging benchmarks are not so perfect as compared to the use of CNNs. The main aim of this chapter is review the existing deep learning techniques to classify and analyze medical imaging data. In this chapter, a comparative analysis of LeNet, AlexNet, and GoogLeNet was done. Furthermore, this review looked at the literature on classifying medicinal anatomy images using CNNs. Based on the various studies the CNNs architecture has better performance with the other architectures in classifying medical images.
Study of the Effectiveness of State Support in the Development and Implementation of Neuro-Educational Technologies

A successful example of a modern school digital laboratory in the field of biophysics and neurotechnologies is the domestic joint development of the DIY kit by BiTronics Lab Company and Laboratory of applied cybernetic systems MIPT. The target audience of consumers of educational neurotechnologies is indicated: schoolchildren, students, specialists of related professions. The results of the implementation of neurotechnologies in the social environment - primary school children - have been studied. A linear regression equation was constructed, which characterizes the dependence of the involved schoolchildren in the work on the study of neurotechnologies from the amount budgetary funds and number of events taken to familiarize with neurotechnologies.
A Quantum Instruction Set Implemented on a Superconducting Quantum Processor

A quantum algorithm consists of a sequence of operations and measurements applied to a quantum processor. To date, the instruction set which defines this sequence has been provided by a classical computer and passed via control hardware to the quantum processor. Here, we demonstrate the first experimental realization of a quantum instruction set, in which a fixed sequence of classically-defined gates perform an operation that is fully determined only by a quantum input to the fixed sequence. Specifically, we implement the density matrix exponentiation algorithm, which consumes $N$ copies of the instruction state $\rho$ to approximate the operation $e^{-i \rho \theta}$ ($\theta$ an arbitrary angle). Our implementation relies on a 99.7% fidelity controlled-phase gate between two superconducting transmon qubits. We achieve an average algorithmic fidelity $\approx 0.9$, independent of the setting of $\rho$, to circuit depth nearly 90. This new paradigm for quantum instructions has applications to resource-efficient protocols for validating entanglement spectra, principal component analysis of large quantum states, and universal quantum emulation.
Machine, Discourse and Power: From Machine Learning in Construction of 3D Face to Art and Creativity

The 3D face reconstruction based on a single image can be done recently by training the machine to learn from the database. The performance and accuracy of 3D facial structure and the geometry have largely improved with the powerful calculation of machine and its expanding data. The 3D face reconstruction can be applied in both still images and moving images and recently such technology draws attention in the art field such as portrait representation, facial recognition, surveillance art, etc. By using artificial intelligence (AI) technology and machine learning, artists are able to create artwork with hybridity characteristic, which consists of both human perspective, and machine creativity, which opens up a new gateway to the art world. The capability of artists in creating artwork, particularly portrait art in this research, has been enhanced in terms of technological and theoretical perspective. Artists can represent faces and understand 3D geometry in new ways by breaking through the traditional limitation; it also provides alternative methods in facial visualization beyond the expectation. Traditional expression and technique can be expended to a new dimension; the technology helps artists to think out of the box and continue to create new movement and ism throughout the art history. Moreover, the traditional belief about the importance of the originality of artists’ mind, sense, perspective and techniques in creating artwork can now be redefined by machine and artificial intelligence. A new form in art and creativity, and even knowledge and history has been developed. This paper attempts to discuss the essential transformation in dimensional imagery and its aesthetic experiences through machine learning means, and the shift of discourse of power from human to machine.
Performance monitoring during categorization with and without prior knowledge: A comparison of confidence calibration indices with the certainty criterion.

Subjective confidence reports are used in numerous research paradigms to examine the extent to which participants are aware of their performance in a task. By examining the discrepancy between objective performance and subjective confidence ratings, inferences can be made about the conditions in which participants have greater explicit knowledge of the representations and processes used to complete a task. In the current study, we examined the effects of prior knowledge on subjective assessments of performance using a categorisation task wherein lists of features that defined exemplars shared latent feature associations on the basis of prior knowledge or had no prior associations. Using 2 methods for computing confidence, we demonstrate the strengths and limitations of these measures of subjective awareness. Whereas our findings replicated the effect of prior knowledge on learning, our results challenge the role of explicit and implicit knowledge suggested by previous research using a similar paradigm. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Further Boosting BERT-based Models by Duplicating Existing Layers: Some Intriguing Phenomena inside BERT

Although Bidirectional Encoder Representations from Transformers (BERT) have achieved tremendous success in many natural language processing (NLP) tasks, it remains a black box, so much previous work has tried to lift the veil of BERT and understand the functionality of each layer. In this paper, we found that removing or duplicating most layers in BERT would not change their outputs. This fact remains true across a wide variety of BERT-based models. Based on this observation, we propose a quite simple method to boost the performance of BERT. By duplicating some layers in the BERT-based models to make it deeper (no extra training required in this step), they obtain better performance in the down-stream tasks after fine-tuning.