Reviewerly/exHarmony huggingface dataset:
Adaptive learning of compressible strings

Suppose an oracle knows a string S that is unknown to us and that we want to determine. The oracle can answer queries of the form “Is s a substring of S?”. In 1995, Skiena and Sundaram showed that, in the worst case, any algorithm needs to ask the oracle σn/4−O(n) queries in order to be able to reconstruct the hidden string, where σ is the size of the alphabet of S and n its length, and gave an algorithm that spends (σ−1)n+O(σn) queries to reconstruct S. The main contribution of our paper is to improve the above upper-bound in the context where the string is compressible. We first present a universal algorithm that, given a (computable) compressor that compresses the string to τ bits, performs q=O(τ) substring queries; this algorithm, however, runs in exponential time. For this reason, the second part of the paper focuses on more time-efficient algorithms whose number of queries is bounded by specific compressibility measures. We first show that any string of length n over an integer alphabet of size σ with rle runs can be reconstructed with q=O(rle(σ+log⁡nrle)) substring queries in linear time and space. We then present an algorithm that spends q∈O(σglog⁡n) substring queries and runs in O(n(log⁡n+log⁡σ)+q) time using linear space, where g is the size of a smallest straight-line program generating the string.
Guided Representation Learning for the Classification of Hematopoietic Cells

Cell classification in human bone marrow microscopy images is a challenging image analysis task due to the number and inter-connection of cell types. While machine learning techniques have vastly higher throughput and could thus be more reliable, humans are intrinsically capable of understanding relations between cell types. In this paper, we propose methods to incorporate such intrinsic model knowledge based on representation learning. To this end, we construct a manually defined, two-dimensional reference embedding, coined embedding guide, which we use together with inverse dimensionality reduction, a distance-based loss and a growing embedding technique. Results show improved classification scores as well as a visually interpretable and clearly defined embedding space.
Museums and Social Inclusion in Korea

The main role of the museum was to collect, preserve, research, educate, and exhibit the physical evidence of the human environment for the purpose of research, education, and enjoyment, and the public was only an object. However, as the times changed, the museum was required to take on a new role to contain the voices of the public. The museum has become a role for social change by engaging people to determine their place in the world, educating them to achieve their potential, and contributing to reforming them in the future. Museums should be able to function as agents of social change by embracing those excluded from society (those who did not participate in cultural and leisure activities). In this study, I examined the relationship between museums and social inclusion, why social inclusion is an issue in museums at this point, and how museums are coping with the problem of social inclusion through the policies of domestic and foreign museums. Certain conditions must be met for the museum to realize social inclusion, and this study considered accessibility and diversity to be important. Based on previous studies, this study identified the form of barriers to museum access and suggested alternatives. At the same time, they emphasized the need to pursue diversity beyond multiculturalism as our society becomes increasingly pluralistic. This study mainly tried to find the implications of social inclusion in museums through the case of the UK.
Intelligent Object Recognition System for Autonomous and Semi-Autonomous Vehicles

Road accidents have long been a significant issue involving loss of life and property. So recent years have seen rapid developments in autonomous and semi-autonomous vehicles. Autonomous vehicles are a comprehensive solution built for safety and comfort on the roads. This solution has many challenges. One of these challenges is to spot and recognize obstacles. As humans do, the only way to discover and recognize these obstacles is to see them. Therefore, vision systems are an essential part of this type of vehicle. This paper proposed a vision-based approach for autonomous vehicles to recognize objects and traffic lights on the road. The proposed system contains three phases: image pre-processing, feature extraction, and classification. In the first phase, some image pre-processing techniques are applied, which consists of three stages: Convert Color images to Grayscale, Histogram Equalization, and Resize Image. The second phase is the extraction of features from images using Principal Component Analysis (PCA). In the third phase, the features that are extracted are used as input to machine learning (ML) classifiers for object recognition. ML algorithms used in this proposed system are Random Forest (RF), Random Tree (RT), and Naive Bayes (NB). The results show that the RF algorithm has the highest classification precision rate, 75%, compared to the NB algorithm 63% and the RT algorithm 53%.
Prototype Generation Based Shift Graph Convolutional Network for Semi-supervised Anomaly Detection

Semi-supervised network is an important branch in video anomaly detection. Previous methods committed to modeling the common feature or distribution of normal data. With the introduction of the pose graph, the model can focus on the behavior of the human body. However, graph embedded networks suffer from the heavy computational cost and could not accurately predict the distribution of normal data. To better tackle these issues, a prototype generation-based graph convolutional network is proposed for anomaly detection, which introduce shift operation and prototype generation module to obtain the distribution of normal data while simplifying the model. Extensive experiments is implemented on ShanghaiTech dataset, the result (76.7 AUC) shows that the proposed approach outperforms most of mainstream models.
Optimizing Secure Decision Tree Inference Outsourcing

Outsourcing decision tree inference services to the cloud is highly beneficial, yet raises critical privacy concerns on the proprietary decision tree of the model provider and the private input data of the client. In this paper, we design, implement, and evaluate a new system that allows highly efficient outsourcing of decision tree inference. Our system significantly improves upon prior art in the overall online end-to-end secure inference service latency at the cloud as well as the local-side performance of the model provider. We first present a new scheme which securely shifts most of the processing of the model provider to the cloud, resulting in a substantial reduction on the model provider's performance complexities. We further devise a scheme which substantially optimizes the performance for secure decision tree inference at the cloud, particularly the communication round complexities. The synergy of these techniques allows our new system to achieve up to <inline-formula><tex-math notation="LaTeX">$8 \times$</tex-math></inline-formula> better overall online end-to-end secure inference latency at the cloud side over realistic WAN environment, as well as bring the model provider up to <inline-formula><tex-math notation="LaTeX">$19 \times$</tex-math></inline-formula> savings in communication and <inline-formula><tex-math notation="LaTeX">$18 \times$</tex-math></inline-formula> savings in computation.
Research on Blockchain in Digital Education Resource Sharing in Colleges and Universities

Blockchain has been widely concerned by the society since its birth. With the development of society, it has shown a wide application prospect in finance, supply chain, medical treatment, education and other fields. The co-construction and sharing of digital education resources is an important way to promote the rapid development of education information. Firstly, the problems encountered in the co-construction and sharing of digital education resources in current universities are analyzed. Then, the model is constructed from the perspective of education through the network structure of blockchain. Building a resource sharing model of university education based on block chain technology can not only increase the sharing range of educational resources, improve the quality of educational resources, but also deposit the intellectual property rights of the uploader. In this research, an improved PBFT algorithm (SPBFT) is designed to reduce the time complexity of the consensus process from O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(N), which effectively reduces the communication overhead. The average delay is from 57ms to 34ms, and the average throughput is from 330TPS to 680TPS.
Two-tier Graph Contextual Embedding for Cross-device User Matching

The cross-device user matching task is to identify the behavior-logs (i.e., behavior sequences) on multiple devices that belong to one real person. Due to its anonymous and long-term properties, most previous methods of learning behavior embeddings cannot effectively capture two important features in the sequences, namely high-order connections and long-range dependencies. To this end, we propose a novel framework called Two-tier Graph Contextual Embedding (TGCE) to solve the above problems simultaneously. In the first tier, we construct behavior evolutionary graphs (BEGs) for behavior sequences and design an order-preserving neighbor aggregation network to collectively model transitions of behaviors with their neighbors. As repeated behaviors can be grouped into single nodes, our model joints neighboring environments around behaviors in a collective way, and behavior embeddings can be enriched. In the second tier, we further build scaled shortcut graphs (SSGs) by refining BEGs with random walk-based edge addition, then a position-aware graph attention network is further imposed on SSGs to facilitate fast information propagation. As distant graph nodes can be directly connected by shortcut edges, we can further capture long-range dependencies. By stacking two graph tiers, our approach can obtain graph contextual embeddings for behaviors to further improve user matching. Experimental results on the benchmark dataset show that our model outperforms various baselines in the user matching task. Our code is released on https://github.com/13061051/TGCE_2021.
Decidable Fragments of Calculi Used in CatLog

CatLog is a categorial grammar parser/theorem-prover developed by Glyn Morrill and his co-authors. CatLog is based on an extension of Lambek calculus. A distinctive feature of this extension is the usage of brackets for controlled non-associativity and a subexponential modality whose contraction rule interacts with bracketing in a sophisticated way. We consider two variants of the calculus, appearing in different versions of CatLog. Both systems are, unfortunately, undecidable in general. We consider fragments where the usage of subexponential is restricted by so-called bracket non-negative/non-positive conditions, prove that these fragments are decidable, and pinpoint their place in the complexity hierarchy. We also consider a more complicated, but more practically interesting problem of inducing (guessing) brackets. For this problem, we prove one decidability and one undecidability result, and leave some open questions for further research.
AI-Based IWrite Assisted English Writing Teaching

With the rapid development of modern information technology, the application of artificial intelligence in the field of education has brought new opportunities and challenges to educators. Iwrite, based on artificial intelligence, is an English writing teaching and evaluation system that includes writing teaching, intelligent correction and automatic grading, which can play a great role in English writing teaching. In order to explore whether AI-based iWrite assisted English writing teaching mode can effectively improve students’ English writing ability, the study adopts two different teaching methods to compare, in order to observe the effect of AI-based iWrite assisted English writing teaching. The results show that AI-based iWrite assisted English writing teaching mode can effectively improve students’ English writing ability, and students are more receptive to it. Therefore, the study suggests that in the future teaching, more and more teachers should follow the trend of educational informatization and integrate AI-based iWrite into their teaching.
A Study of General Data Improvement for Large-Angle Head Pose Estimation

Predicting Euler angles of head pose using end-to-end CNN from a single RGB image is a popular application in recent years. However, the existing methods ignored the information about the rotation order contained in the Euler angles, always following the traditional pitch-yaw-roll order. They also neglected the error sources from outlier samples with large-angle poses. We analyzed current shortcomings and made suggestions for improvement from the perspective of data distribution. We studied the influence of different rotation orders on the data distribution and showed choosing an appropriate rotation order to learn head pose can significantly optimize the data distribution and improve the prediction accuracy. Then a data enhancement method was proposed to increase the large-angle poses by rotating the 2D images randomly and solving the corresponding head poses, which can improve network performance on the large-angle poses. Evaluated on two popular networks and different datasets, our methods were proved to be effective and general.
Towards Category and Domain Alignment: Category-Invariant Feature Enhancement for Adversarial Domain Adaptation

Adversarial domain adaptation has made impressive advances in transferring knowledge from the source domain to the target domain by aligning feature distributions of both domains. These methods focus on minimizing domain divergence and regard the adaptability, which is measured as the expected error of the ideal joint hypothesis on these two domains, as a small constant. However, these approaches still face two issues: (1) Adversarial domain alignment distorts the original feature distributions, deteriorating the adaptability; (2) Transforming feature representations to be domain-invariant needs to sacrifice domain-specific variations, resulting in weaker discriminability. In order to alleviate these issues, we propose category-invariant feature enhancement (CIFE), a general mechanism that enhances the adversarial domain adaptation through optimizing the adaptability. Specifically, the CIFE approach introduces category-invariant features to boost the discriminability of domain-invariant features with preserving the transferability. Experiments show that the CIFE could improve upon representative adversarial domain adaptation methods to yield state-of-the-art results on five benchmarks.
A Gospel for MOBA Game: Ranking-Preserved Hero Change Prediction in <i>Dota 2</i>

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dota 2</i> is one of the most popular multiplayer online battle arena games, in which players of different teams controlling heroes fight each other to pursue a championship. To enrich the diversity of heroes and provide a balanced battle environment, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dota 2</i> game designers keep changing the attributes or skills of heroes in constantly updated game versions. Since <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dota 2</i> is an intricate game, and numerous factors are involved in a match, it is challenging to figure out how to adjust heroes to meet the balance. As far as we know, no effort from intelligent learning perspective has been made to judge whether a hero should be changed in the next game version. This article proposes a ranking-preserved method to predict whether a hero should be enhanced, weakened, or unchanged. Specifically, a unified ranking-preserved temporal graph convolutional network model containing ranking preservation, graph convolutional network, and long short-term memory is designed to generate a ranking list of all the heroes, which indicates the strength of heroes as well as which heroes to be enhanced or weakened. The experiments on match records show that our approach provides a high-quality prediction and performs better than other baseline models. For game players, our model can give them a view of which heroes are more powerful currently and help them make better choices in choosing heroes. For game designers, our model can provide statistical supports and model interpretation for hero adjustment.
A Study on the Performance of Grey Wolf Optimizer

In recent years there is an enormous increase in the emergence of non-deterministic search methods. The effective way of animals in problem-solving (like discovering the shortest path to the food source) has been examined by scientists and swarm intelligence has become a research field that imitates the behaviour of animals in swarm. The moth-flame optimization (MFO) algorithm, salp swarm algorithm (SSA), firefly algorithm (FFA), bat (BAT) algorithm, cuckoo search (CS) algorithm, genetic algorithm (GA), and grey wolf optimizer (GWO) are some of the swarm intelligence based non-deterministic methods. In the present study, the seven methods above are investigated separately. Five mathematical functions are resolved individually by these seven methods. Each algorithm is run 30 times in each benchmark function. The performances of these optimization methods are evaluated and compared within each function individually. Performances of algorithms over convergence are compared by plotting convergence rate graph and boxplots of methods for each function. Considering most of the functions, GWO is observed to be stronger than other algorithms.
Style Transfer Analysis Based on Generative Adversarial Networks

Style transfer means using a neural network to extract the content of one image and the style of the other image. The two are combined to get the final result, broadly applied in social communication, animation production, entertainment items. Using style transfer, users can share and exchange images; painters can create specific art styles more readily with less creation cost and production time. Therefore, style transfer is widely concerned recently due to its various and valuable applications. In the past few years, the paper reviews style transfer and chooses three representative works to analyze in detail and contrast with each other, including StyleGAN, CycleGAN, and TL-GAN. Moreover, what function an ideal model of style transfer should realize is discussed. Compared with such a model, potential problems and prospects of different methods to achieve style transfer are listed. A couple of solutions to these drawbacks are given in the end.
Comparison of Different Encryption Algorithm and Proposing an Encryption Algorithm

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Sentiment Analysis of Snapchat Application's Reviews

Sentiment analysis is a process of extracting opinion and subjectivity knowledge from user generated text content without the need to monitor the reviews manually. It can help to obtain an overview on performance of a product or subject based on the reviews from users. The aim of this study is to classify the Snapchat application's reviews into different polarities which are positive, neutral or negative. Next, the most frequent words are identified. Furthermore, Multinomial Naïve Bayes and Random Forest classification algorithm are used to predict the user's rating. The performances of the classification models are evaluated using accuracy, precision, recall and F1-score. The results showed majority of the Snapchat users had a positive experience with the total of 6037 positive reviews. Based on the performance measures, Multinomial Naïve Bayes classification algorithm performed slightly better than the Random Forest classification algorithm in predicting the rating of Snapchat application. Overall, both of the classification algorithms have average performance in predicting user's rating for Snapchat application.
Exploiting Visual Context to Identify People in TV Programs

Television is a medium that is implicitly highly codified. Every TV program has its own visual identity that is often rich in information; most of the time, a single frame extracted from a TV broadcast contains enough information for a human agent to determine the genre of the program, and sometimes even to predict who is likely to appear in it. Our goal is to exploit the visual context of TV programs to help identify the people appearing in them.
Best Practices for Cloud Data Protection and Key Management

Cloud has fundamentally transformed digital and cybersecurity landscape. Enterprises continue to move their data and applications to the cloud so they can utilize and pay for only needed resources, thereby avoiding large upfront investment. Some also believe that in addition to its promise of demand elasticity, the cloud also offers security of data. We analyze this security aspect in this paper. Protection of data stored in the cloud is intertwined with the generation, management and lifecycle of cryptographic keys that encrypt this data. We list various options available for protection of data, along with their relative strengths and weaknesses. These options are explained with examples from three main cloud service providers; Google, Azure and AWS. We highlight the importance of separation of duty and how cryptographic keys can and should be managed by trusted third parties. This not only increases security but can also facilitate regulatory compliance. While describing security best practices for protecting sensitive data in the public cloud, the paper also explains concepts such as BYOK, HYOK, key brokering and Root of Trust. It describes the level of data protection that can be achieved by using cloud native encryption and key management service, and how security can be enhanced by allowing customers to take direct responsibility for and control over their keys.
Methods of Achieving Successful Professional Communication from the Perspective of Intercultural Communication

Professional communication is often understood as just an ability to read scientific texts and to have terminology knowledge. However, scientists of different nationalities have a different mentality. In order to interact successfully in a professional sphere, it is necessary not only to have the above-mentioned knowledge and skills, but also to be able to communicate with another culture representatives. Understanding the business partner’s mentality, as well as presenting your own identity in an accessible way is the key to fruitful cooperation. This is possible only if a Russian partner is creative and able to go beyond traditional ideas, patterns, relationships and create new meaningful ways and interaction techniques. In other words, this can be called creative thinking. The purpose of the study is to determine the levels of students’ cultural self-identity, offer the author’s approach, principles and assignments for designing a second language course. The leading research method was an experimental training conducted in both the intervention and the control group. The questionnaire method, methods of mathematical statistics and graphical results representation were used as well. Nizhny Novgorod State Linguistics University was the study experimental basis. The experimental training results showed an increase in the level of students’ cultural self-identity. The correlation between a sufficient level of cultural self-identity and professional success was proved. Thus, the intercultural dialogue implementation is a factor in the success of second-language professional communication.