Reviewerly/exHarmony huggingface dataset:
Stochastic Neural Variational Learning of Noisy-OR Bayesian Networks for Images

Bayesian networks are not only useful as causal probabilistic models but also promising as models of the cerebral cortex. This paper addresses the problem of unsupervised learning in Bayesian networks with noisy-OR conditional probability tables. We employ neural variational inference and learning (NVIL), in which intractable posterior distribution is approximated by the output of a neural network. Both the noisy-OR Bayesian network and the posterior distribution neural network are optimized to maximize the variational lower bound of the true log-likelihood. To examine the effectiveness of the proposed method, we used the MNIST handwritten digit dataset for unsupervised learning of noisy-OR Bayesian networks. We confirmed that noisy-OR Bayesian networks with up to 128 latent variables can learn the given dataset using NVIL. Interestingly, the latent variables of the noisy-OR Bayesian networks learned fragments of digit images as their representation. These representations are easier to interpret than the representations acquired by sigmoid belief networks.
R-Calculus for Description Logic

In the last chapter, $$\mathbf{T}^{\texttt {t}}$$ and $$\mathbf{S}^{\texttt {f}}$$ are complementary, that is, a theory $$\Delta $$ is either $$*$$ -valid or $$*^{\texttt {f}}$$ -satisfiable; and sound, complete with respect to $$*$$ -validity and $$*^{\texttt {f}}$$ -satisfiability, respectively. Because deductions in $$\mathbf{T}^{\texttt {t}}$$ and $$\mathbf{S}^{\texttt {f}}$$ are of finite length, whether $$\Delta $$ is provable in $$\mathbf{T}^{\texttt {t}}$$ or $$\mathbf{S}^{\texttt {f}}$$ is semi-decidable, and decidable. By combining $$\mathbf{T}^{\texttt {t}}$$ and $$\mathbf{S}^{\texttt {f}}$$ into one system $$\mathbf{R}^{\texttt {f}}$$ is meaningful.
White-Box Encryption Scheme for Resource-Constrained IoT Devices

The Internet of Things (IoT) offers tremendous benefits to end-users, but it also creates enormous security risks. Many embedded and resource-constrained devices are used in IoT, and they are often deployed in an insecure and remote environment. A White-Box (WB) attack model, where an attacker has complete control over the built-in cryptosystem, enters the scene at this point. As a result, a WB attacker who captures these devices can thwart the cryptographic implementation and obtain the cryptographic keys. To defend against such attacks, the White-Box Cryptography (WBC) encryption technique is used. In this paper, we explore the discovered issues in the context of WB research and its relevance to resource-constrained IoT devices. We describe the main features of our proposed WB-Elliptic Curve Encryption method and explore its future potentials.
Accelerated Reward Policy (ARP) for Robotics Deep Reinforcement Learning

Reward policy is a crucial part for Deep Reinforcement Learning (DRL) applications in Robotics. The challenges for autonomous systems with “human-like” behavior have posed significant need for a better, faster, and more robust training based on optimized reward function. Inspired by the Berkeley and Google’s work, this paper addresses our recent development in reward policy/function design. In particular, we have formulated an accelerated reward policy (ARP) based on a non-linear functions. We have applied this reward function to SAC (Soft Actor Critic) algorithm for 6 DoF (Degree of Freedom) robot training in simulated environment using Unity Gaming platform and a 6 DoF robot. This nonlinear ARP function gives bigger reward to accelerate the robot’s positive behavior during the training. Comparing to the existing algorithm our experimental results demonstrated faster convergence and bigger, better accumulative reward. With limited experimental data, the results show improved accumulative reward function as much as 2 times of the previous results.
DEEPA: A Deep Neural Analyzer for Speech and Singing Vocoding

Conventional vocoders are commonly used as analysis tools to provide interpretable features for downstream tasks such as speech synthesis and voice conversion. They are built under certain assumptions about the signals following signal processing principle, therefore, not easily generalizable to different audio, for example, from speech to singing. In this paper, we propose a deep neural analyzer, denoted as DeepA – a neural vocoder that extracts F0 and timbre/aperiodicity encoding from the input speech that emulate those defined in conventional vocoders. Therefore, the resulting parameters are more interpretable than other latent neural representations. At the same time, as the deep neural analyzer is learnable, it is expected to be more accurate for signal reconstruction and manipulation, and generalizable from speech to singing. The proposed neural analyzer is built based on a variational autoencoder (VAE) architecture. We show that DeepA improves F0 estimation over the conventional vocoder (WORLD). To our best knowledge, this is the first study dedicated to the development of a neural framework for extracting learnable vocoder-like parameters.
Research on the Strategy of Bidding in the Game of Dou dizhu

As an important field of artificial intelligence, computer game is an important platform for testing the development level of artificial intelligence. In the field of incomplete information of computer games, Dou dizhu game is typical. Bidding is a key link in the game, in which the strategy of bidding plays an important role in the outcome of the game. In this paper, the problem of bidding in Dou dizhu game is studied, and the definition of points representing the quality of hand card is given. Firstly, according to the hand card information, the split priority is set according to different card types, so as to obtain different card splitting schemes, and the hand score of each card splitting scheme is calculated. The scheme with the lowest score is the optimal card splitting scheme. Then, according to this score, the corresponding bidding is given. The experiment is compared with a large number of real-life data, and the results show that the matching degree is 85%, which shows that the scheme proposed in this paper is feasible.
Research on Problem Matching Classification Based on Artificial Intelligence Technology

In recent years, due to the continuous development of the Internet, electronic text information based on natural language expression has gradually increased. Among so many electronic text information, one of the biggest goals based on natural language processing is to effectively obtain and manage this information. For the questions raised by users, natural language processing technology needs to be used for in-depth research and sorting. The classification of questions will be extra important here, and it is the basis of information retrieval. Research on the theory and engineering application of problem classification tasks will also be of great significance. After preprocessing and vectorization of the problem, this paper uses the long and short-term memory model to complete the classification of the problem.
Improving Embedded Convolutional Object Detectors Through Low-res Training

Convolutional object detectors have been compared in a plet-hora of articles, but when taken in consideration within embedded applications a compromise between speed and accuracy must take place. From a “no free lunch” perspective, it is agreed that lower resolution images increase speed while sacrificing accuracy and resolution. In here we compare MobileNet-SSD and Yolov3-tiny over two portable controllers, Raspberry Pi and Nvidia TX2, by systematically reducing the picture resolution and changing the distance to target. Our results point to the existence of an optimal lower resolution training set, capable of achieving best accuracy with a modest speed compromise.
Quantum Speedup and Limitations on Matroid Property Problems

Matroid theory has been developed to a mature branch of mathematics and has extensive applications in combinatorial optimization. On the other hand, quantum computing has attracted much attention and has been shown to surpass classical computing on solving some computational problems. Surprisingly, crossover studies of the two fields seem to be missing in the literature. This paper initiates the study of quantum algorithms for matroid property problems. It is shown that quadratic quantum speedup is possible for the calculation problem of finding the girth or the number of circuits (bases, flats, hyperplanes) of a matroid, and for the decision problem of deciding whether a matroid is uni-
BERT-Assisted Semantic Annotation Correction for Emotion-Related Questions

Annotated data have traditionally been used to provide the input for training a supervised machine learning (ML) model. However, current pre-trained ML models for natural language processing (NLP) contain embedded linguistic information that can be used to inform the annotation process. We use the BERT neural language model to feed information back into an annotation task that involves semantic labelling of dialog behavior in a question-asking game called Emotion Twenty Questions (EMO20Q). First we describe the background of BERT, the EMO20Q data, and assisted annotation tasks. Then we describe the methods for fine-tuning BERT for the purpose of checking the annotated labels. To do this, we use the paraphrase task as a way to check that all utterances with the same annotation label are classified as paraphrases of each other. We show this method to be an effective way to assess and revise annotations of textual user data with complex, utterance-level semantic labels.
Experimental demonstration of free-space two-photon interference

Quantum interference plays an essential role in understanding the concepts of quantum physics. Moreover, the interference of photons is indispensable for large-scale quantum information processing. With the development of quantum networks, interference of photons transmitted through long-distance fiber channels has been widely implemented. However, quantum interference of photons using free-space channels is still scarce, mainly due to atmospheric turbulence. Here, we report an experimental demonstration of Hong-Ou-Mandel interference with photons transmitted by free-space channels. Two typical photon sources, i.e., correlated photon pairs generated in spontaneous parametric down conversion (SPDC) process and weak coherent states, are employed. A visibility of 0.744 ± 0.013 is observed by interfering with two photons generated in the SPDC process, exceeding the classical limit of 0.5. Our results demonstrate that the quantum property of photons remains even after transmission through unstable free-space channels, indicating the feasibility and potential application of free-space-based quantum interference in quantum information processing.
Regression Algorithm Based on Self-Distillation and Ensemble Learning

Low-dimensional feature regression is a common problem in various disciplines, such as chemistry, kinetics, and medicine, etc. Most common solutions are based on machine learning, but as deep learning evolves, there is room for performance improvements. A few researchers have proposed deep learning-based solutions such as ResidualNet, GrowNet and EnsembleNet. The latter two methods are both boost methods, which are more suitable for shallow network, and the model performance is basically determined by the first model, with limited effect of subsequent boosting steps. We propose a method based on self-distillation and bagging, which selects the well-performing base model and distills several student models by appropriate regression distillation algorithm. Finally, the output of these student models is averaged as the final result. This integration method can be applied to any form of network. The method achieves good results in the CASP dataset, and the R2(Coefficient of Determination) of the model is improved from (0.65) to (0.70) in comparison with the best base model ResidualNet.
Computer Assisted Design and Implementation of Diagnostic Evaluation Model in Online Test

This paper preliminarily completes the design of English diagnostic evaluation scheme and related algorithms, and puts forward a new evaluation model to evaluate learners&#x0027; learning status, infer learners&#x0027; knowledge barriers, and adjust learners&#x0027; learning strategies for College English Test. After using the diagnostic test system, learners can obtain the corresponding diagnostic report. At the same time, the system will analyze the learners&#x0027; scores and recommend targeted test questions. Through more accumulation of learners&#x0027; practice information on the diagnostic test system, it will provide a basis for the further improvement and perfection of the evaluation scheme, so as to improve the effect of diagnostic evaluation.
Postscript: Revisiting the Concept of the Edited Collection

This Postscript revisits the concept of the edited collection based on a set of conventions that emerged in the nineteenth century. Basically, the edited collection is a collection of original scholarly articles. As a book genre, it has become a standard vehicle in academic publishing. In this Postscript we support the call for greater openness, collaboration, trust and we return to the concept of ‘The Edited Collection in the Age of the Postdigital-Biodigital Convergence’. Finally, we reflect on our experiment with the genre of the edited collection conducted during the production of this book.
Diversity Enhanced Image Captioning with Adversarial Learning

Image captioning aims at analysing image content and translating it in the form of natural language. The process of caption generation is the conversion process from image domain to text domain. Currently, the image captioning has made great achievement but in many case the generated image caption content is simple and lacks diversity. Therefore, in this paper, we propose a diversity enhanced image caption generation method by adversarial learning. The method proposed in this paper solves the problem by minimizing the difference between captions, while the intra-group difference and the inter-group difference are employed as the criteria to improve the diversity of generated caption. The method is optimized with generative adversarial networks framework. We evaluate the proposed method on the CUB-200 dataset with accuracy and diversity metric and the results shows that the proposed method could not only improve the diversity of generated caption but also ensure its semantic accuracy.
Exploring Multiple Genres Text Classification: Classifying 61 genres of Mobile App Description based on Naïve Bayes and Count Vectorizer

Text classifying is a classic NLP problem. In our following research, we aimed at a multiple classification problem. We used the list of descriptions of a vast number of apps as a data set. and applied several different machine learning algorithms to analyze the data. Firstly, we did the preprocess of the data to mark the genres, there are 59 genres in the dataset in total. Then, we vectorized the data with both Count Vectorizer and TfidfVectorizer and use the vectorized dataset to train models through Naïve Bayes, SVM, Random Forest and KNN. Then, we did some optimization for the parameters and determined Naïve Bayes the algorithm with best accuracy and performance for this problem. Finally, we further optimized the parameter for the Naive Bayes as well as reduced the redundant genres in our dataset and get the accuracy of about 60.276%.
Bursty Events Detection with the Field of Mobile Customer Service

In the field of mobile customer service, the increase of traffic volume and the drop of connection rate caused by uncertain factors are called bursty events. When bursty events occur, detecting the bursty events timely and proactively can improve resource scheduling efficiency, connection rate, and customer satisfaction. The existing bursty events detection methods are mainly dependent on human experience, which detect events untimely and incompletely. In this paper, an unsupervised approach of detecting bursty events based on speech-to-text data is proposed, which makes good use of multiple dimensional features of the field to detect and track bursty events. Using our method, we achieve performances of 90.46%, 86.22% and 86.15% w.r.t. the average precision, recall and F1 score respectively. The experimental results demonstrate that the proposed method is effective to detect bursty events among considerable speech-to-text data.
Sexist Hate Speech: Identifying Potential Online Verbal Violence Instances

Online communication provides space for content dissemination and opinion sharing. However, the limit between opinion and offense might be exceeded, characterizing hate speech. Moreover, its automatic detection is challenging, and approaches focused on the Portuguese language are scarce. This paper proposes an interface between linguistic concepts and computational interventions to support hate speech detection. We applied a Natural Language Processing pipeline involving topic modeling and semantic role labeling, allowing a semi-automatic identification of hate speech. We also discuss how such speech qualifies as a type of verbal violence widespread on social networks to reinforce a sexist stereotype. Finally, we use Twitter data to analyze information that resulted in virtual attacks against a specific person. As an achievement, this work validates the use of linguistic features to annotate data either as hate speech or not. It also proposes using fallacies as a potential additional feature to identify potential intolerant discourses.
A Decision Making Paradigm for Software Development in Libraries

Framing a technology question as a simple choice between developing an in- house application system and off-the- shelf proprietary system, or simply put, as a choice between build and buy, runs the risk of ignoring myriad options available in between the two extremes. In this era of cloud computing and run anything-as- a-service model, the very notion of developing an in-house application would raise a few eyebrows among C- level executives. How then can academic libraries, under mounting pressure to demonstrate their value (Oakleaf, 2010), justify investments in software development in particular? What follows in these sections is a brief discussion on the importance of investing in software development in libraries, three mini-case studies demonstrating the wide possibilities of integrating software development in library operations and a non- prescriptive model to assess which projects may be worth pursuing from the software development standpoint.
Exploring the Limits of Machine Learning in the Prediction of Solar Radiation

Predicting solar radiation at diverse time horizons is crucial for optimizing solar energy integration, ensuring grid stability, and regulating energy markets. Two main levels of time granularity are usually recognized as requiring different treatment: solar nowcasting for predictions up to 6 h, and solar forecasting for predictions beyond 6 h. Solar nowcasting typically relies on machine learning methods, while Numerical Weather Prediction (NWP) models are considered better suited for solar forecasting. The goal of this study was to explore the limits of machine learning in solar forecasting. Our results show that machine learning methods can be profitably used for predicting solar radiation beyond 6 h, with comparable performances to NWP models for day-ahead solar forecasting.
Coverless Steganography Method based on the Source XML File Organization of OOXML Documents

Existing search-based coverless text steganography algorithms according to the characteristics of the text, do not need to modify the carrier, and have good resistance to detection, but they rely on a large text data set and have a limited hiding capacity. For this reason, this paper proposes a coverless steganography method based on the source XML file organization of the OOXML documents from a new perspective. It analyzes the organization of OOXML documents, and uses the differences of organization to construct the mapping between documents and secret information, so as to realize the coverless information hiding. To achieve the efficiency of information hiding, a compound tree model is designed and introduced to construct the OOXML document category library. Compared with the existing coverless information hiding methods, the text set size that this method relies on is significantly reduced, and the flexibility of the mapping is higher under the similar hiding capacity.
Unsupervised Discovery of Non-Trivial Similarities between Online Communities

In this work we introduce C3 – a novel unsupervised approach for community comparison. C3 creates contextual pairwise representations by aligning communities and tuning word embeddings according to both the lexical context, and the social context reflected by the community’s structure and the community engagement patterns. C3 is evaluated over a dataset of 1565 active Reddit communities, comparing results against three competitive models. We show through an array of experiments and validations that C3 recovers nuanced and not-trivial similarities between communities that are not captured by other methods. We complement our quantitative results with a qualitative analysis, discussing recovered non-trivial similarities between community pairs such as (opiates:adhd) , (babyBumps:depression) , and (wallStreetBets:sandersForPresident) , all of which are not recovered by other models. This qualitative analysis demonstrates the exploratory power of our model.
A Traceable and Revocable Multi-Authority Access Control Scheme with Privacy Preserving for Mhealth

Mobile healthcare (mHealth) is a smart health system, which makes people have a storage and share in their personal health data (PHD) in a cloud for medical treatment rapidly through mobile 5G-enabled Industrial IoT equipments. But key hosting problem and privacy leakage problem issued by malicious users and revoked users bring new security challenges. Traceability and accountability to attackers have been an important issue in mHealth system. Among the solving methods, most of them still face key abusing and privacy leakage problems. In this research, an expressive privacy preserving attribute-based traceable approach is proposed, where it keeps PHD confidentiality, eliminates the key delegation issue and prevents the key from being abused, allowing for fine-grained access control to the shared data in mHealth. Under this framework, we imprint a robust unforgeable signature in the secret key to assure the tracked userâ€™s undeniability. We also add a revocable function that can efficiently remove the malicious users by only updating party of ciphertext, and unrevoked users still can access the encryption data using original key. To achieve privacy protection, a bloom filter is used to hide the access policy. Security analysis and performance comparisons show the proposed method is secure and efficient.