Reviewerly/exHarmony huggingface dataset:
Algorithms for Tracking Changes in Preference Distributions

As described in Sect. 1.1, one of the core problems in music recommendation over time, and in content recommendation in general, is that the distributional properties of music, and people’s musical tastes, change over time in ways that are nontrivial to track and predict. This informational challenge is a special case of concept drift—the change, either abrupt or gradual, in the underlying structure of data. Concept drift is a common and fundamental problem in machine learning in general, so any solutions designed to combat drift in content recommendation, even ones specific for that setting, are broadly applicable to ML research in general. In this chapter I focus on the application of reinforcement learning approaches to handle concept drift, specifically through model retraining, both in its general context and directly with respect to tracking people’s temporal listening habits as reflected by a real world dataset.
Web Information Recommendation Evaluation Model Based on Multifactor Decision Making

There is a certain gap between the information recommendation of web platform and the real demand of users, because recommendation systems lack historical transaction data and excellent recommendation algorithm. In order to improve users' satisfaction for information recommendation on web platform, a web information recommendation evaluation model is proposed, in which users' experience as the evaluation basis of information recommendation service quality. Firstly, a web information recommendation evaluation model is constructed, which consists of three factors: all customers' feedback on the commodity, direct relevance analysis between customer and commodity, and the customer's all transaction feedback. Secondly, the particle swarm optimization algorithm is applied to solve the optimal weight of each factor in the information recommendation evaluation model, then the evaluation result of information recommendation is calculated, which can provide decision-making basis for reasonably dealing with the information recommendation. Finally, simulation results show that the web information recommendation evaluation model based on multifactor decision making is effective, and it can improve users' satisfaction for web information recommendation significantly.
A Parallel Turbo Product Codes Decoder Based on Graphics Processing Units

Turbo product codes (TPC) are a class of forward error correction (FEC) codes. They have good bit error rate (BER) performance at high code rate. It is relatively simple to implement the encoder of TPC and the decoding complexity of their decoder is reasonable. Therefore, TPC are widely used in various places such as satellite communication systems and data storage systems. In this paper, a parallel TPC decoder based on GPUs is proposed. All rows or columns of the two-dimensional product code matrix are decoded simultaneously in this proposed decoder. A parallel elementary decoder is designed to simplify the decoding process of TPC which are constructed by extended Hamming codes. The calculations of test patterns and valid codewords are parallelled to reduce decoding latency. In order to further improve the decoding throughput, we present the multi-channels TPC decoder. The performance of the parallel decoder is measured on different GPUs. The experiment result shows that the decoding latency is reduced significantly compared with the TPC decoder based on a CPU. In addition, throughputs of proposed GPU decoder achieve 30Mbps on Nvidia RTX 2080 Ti and 38 Mbps on Nvidia Titan V, which are 44 times and 54 times of the CPU-based decoder.
An inexpensive XRF lab for undergraduates and other educational activities at tarleton’s nuclear laboratory

Since 2001, Tarleton State University’s nuclear laboratory has been providing educational labs and research activities to support both physics and non-physics majors. Using Internet based multichannel analyzers, the lab supports not only Tarleton students, but also undergraduates at six other Texas institutions in the Texas Physics Consortium (TPC) of which Tarleton is a founding member. A discussion of the facility, various undergraduate activities, and the laboratory’s K-12 outreach activities will be presented. Among those activities to be discussed is the development of an XRF (x-ray fluorescence lab) lab to measure the charge on an electron that can be performed by high schools or undergraduate institutions without x-ray machines or non-exempt radioactive sources and its use in our high school summer physics camp.
13.1: <i>Invited Paper:</i> Multigrid Backprojection Super‐Resolution and Deep Filter Visualization

We introduce a novel deep‐learning architecture for image upscaling by large factors (e.g. 4x, 8x) based on examples of pristine high‐resolution images. Our target is to reconstruct high‐resolution images from their downscale versions. The proposed system performs a multi‐level progressive upscaling, starting from small factors (2x) and updating for higher factors (4x and 8x). The system is recursive as it repeats the same procedure at each level. It is also residual since we use the network to update the outputs of a classic upscaler. The network residuals are improved by Iterative Back‐Projections (IBP) computed in the features of a convolutional network. To work in multiple levels we extend the standard back‐projection algorithm using a recursion analogous to Multi‐Grid algorithms commonly used as solvers of large systems of linear equations. We finally show how the network can be interpreted as a standard upsampling‐and‐filter upscaler with a space‐variant filter that adapts to the geometry. This approach allows us to visualize how the network learns to upscale. Finally, our system reaches state of the art quality for models with relatively few number of parameters.
Proactive Minimization of Convolutional Networks

Optimizing the performance of convolutional neural networks (CNNs) in real applications (i.e., production programs) is crucial. The desired network can perform a task while having minimal evaluation time and resource requirement. The evaluation time of a network strongly depends on the number of layers and the number of convolutional kernels in each layers. Therefore, by minimizing the network while keeping its accuracy high is a frequent task. In this paper, we present variations of a method for the automatic minimization of convolutional networks. Our method minimizes the neural network by omitting convolutional kernels during the training process, while also keeping the quality of the results high.
Theatre Art In The Information Age: Youth Audience Perception Features

The article reflects the long-term experience of studying the youth audience artistic perception of modern theatrical art. The research relevance is confirmed by the change in the modern adolescents perception of art works, which are determined by the qualitative changes in the sociocultural situation in the information society. The authors are looking for an answer to the question: “How do teens perceive modern performances staged from classical plays included in the school curriculum for compulsory study?” (this article presents the material on the A. Griboyedov play “Woe from Wit”). The dissonance that arises between the students’ needs in mastering theatrical art and the possibilities that Russian theatres provide confirms the insufficiency of the developing artistic perception ways among students in the educational system. The authors of the article reveal the interests and high school students perception peculiarities of both classical productions and postmodern experimental performances. To study of the adolescents artistic perception features, the diagnosis of artistic perception was used, presented in the article and built on the theory basis of the relationship between real and theatrical reality. The article presents the results of research obtained in the course of statistical data analysis and pedagogical observation, such as the levels of modern adolescents artistic perception of theatrical art; the artistic perception development stages of the play within the pedagogical conditions complex and the basic specific pedagogical conditions of the Moscow region students communication development in mastering theatrical art sphere.
Enhanced Topic Modelling using Dictionary For Questions and Answers Problem

Making Questions and Answers (QA) with large data and a broad context of problems can cause the desired document to sometimes be irrelevant. QA in terms of religious-social issues have a broad context, so they need to be firstly introduced to the topic. However, the questions raised in the Questions and Answers problem have short text criteria that must be correctly identified by the topic according to the relevant answers. In this paper, we proposed topic modeling for questions and answers with improved term weighting in special words in religious-social problems. The process consisted of preprocessing, making improved dictionary and modeling topic based on dictionary. The result obtained was in the form of topics from input short text which assisted in taking relevant topics, so that correct answers to the questions were obtained.
Rating Computer Science via Chess

Computer chess was originally purposed for insight into the human mind. It became a quest to get the most power out of computer hardware and software. The goal was specialized but the advances spanned multiple areas, from heuristic search to massive parallelism. Success was measured not by standard software or hardware benchmarks, nor theoretical aims like improving the exponents of algorithms, but by victory over the best human players. To gear up for limited human challenge opportunities, designers of chess machines needed to forecast their skill on the human rating scale. Our thesis is that this challenge led to ways of rating computers on the whole and also rating the effectiveness of our field at solving hard problems. We describe rating systems, the workings of chess programs, advances from computer science, the history of some prominent machines and programs, and ways of rating them.
An MCTS-based Adversarial Training Method for Image Recognition

We present an adversarial training algorithm based on Monte Carlo Tree Search. We illustrate the robustness of the algorithm by studying its resistance to adversarial examples in the context of the MNIST and CIFAR10 datasets. For MNIST, after 2000 epochs the experimental results showed an average improvement of efficiency of 21.1% when compared to PGD. For CIFAR10, after 7000 epochs we obtained an average improvement of efficiency of 9.8% compared to PGD. We further compare the robustness of the algorithm against previous work against various attack methods. The results suggest that the adversarial training method here introduced is not only robust with respect to adversarial examples but also efficient during training.
Attribute-Based Data Retrieval with Keyword Search over Encrypted Data in Cloud

This paper presents searchable encryption with access control under multi-user setting with secure index represented by Hash Table structure encrypted by $$ RC2 $$ encryption algorithm and combine with Cipher-text Policy Attribute Based Encryption $$ \left( {CP - ABE} \right) $$ . It can be efficiently constructed and implemented. Extensive analysis on selected data shows that proposed scheme is efficient and accurate.
A Graph-Based Node Identification Model in Social Networks

An enormous amount of data is being generated in many social networking sites. Among them, Twitter is the most data generated platform, which includes commercial data, business data, events, and polls. Various tasks can be done with the generated data. One among them is finding the trending keyword in the huge data. To obtain the trending keyword, we have to consider certain factors to improve the model performance, which includes centrality measure, term frequency, and the position of the nodes in the network. Therefore, we propose a graph-based keyword extraction approach referred to as Multi-Attribute Keyword Extraction (MAKE) which determines the significance of a keyword with the aid of collectively taking various influencing parameters. The proposed graph-based approach is more accurate in finding the importance of a node in the network including the above-mentioned factors.
On Deciding Admissibility in Abstract Argumentation Frameworks

In the context of abstract argumentation frameworks, the admissibility problem is about deciding whether a given argument (i.e. piece of knowledge) is admissible in a conflicting knowledge base. In this paper we present an enhanced backtracking-based algorithm for solving the admissibility problem. The algorithm performs successfully when applied to a wide range of benchmark abstract argumentation frameworks and when compared to the state-of-the-art algorithm.
A Distant Supervised Relation Extraction Model with Two Denoising Strategies

Distant supervised relation extraction has been an effective way to find relational facts from text. However, distant supervised method inevitably accompanies with wrongly labeled sentences. Noisy sentences lead to poor performance of relation extraction models. Though existing piecewise convolutional neural network model with sentence-level attention (PCNN+ATT) is an effective way to reduce the effect of noisy sentences, it still has two limitations. On one hand, it adopts a PCNN module as sentence encoder, which only captures local contextual features of words and might lose important information. On the other hand, it neglects the fact that not all words contribute equally to the semantics of sentences. To address these two issues, we propose a hierarchical attention-based bidirectional GRU (HA-BiGRU) model. For the first limitation, our model utilizes a BiGRU module in place of PCNN, so as to extract global contextual information. For the second limitation, our model combines word-level and sentence-level attention mechanisms, which help get accurate sentence representations. To further alleviate the wrongly labeling problem, we first calculate the co-occurrence probabilities (CP) between the shortest dependency path (SDP) and the relation labels. Based on these co-occurrence probabilities, two denoising strategies are proposed to reduce noise interference respectively from aspect of filtering labeled data and integrating CP information into model. Experimental results on the corpus of Freebase and New York Times (Freebase+NYT) show that the HA-BiGRU model outperforms baseline models, and the two co-occurrence probabilities based denoising strategies can improve robustness of HA-BiGRU model.
Viewpoint-robust Person Re-identification via Deep Residual Equivariant Mapping and Fine-grained Features

Existing person re-identification methods usually directly calculate the similarities of person pictures regardless of their viewpoints. However, matching persons under different viewpoints is difficult since it is intrinsically hard to directly learn a representation which is geometrically invariant to large viewpoint variations. In this paper, we explicitly take viewpoint information into account and propose a novel Deep Residual Equivariant Mapping and Fine-grained Features (DREMFF) approach for viewpoint-robust person re-identification. Specifically, DREMFF hypothesizes that there exists inherent mapping between different viewpoints of a person, and consequently, the global representation discrepancy of a person under different viewpoints will be bridged through equivariant mapping by adaptively adding residuals to original representation according corresponding angle deviation. What's more, based on attention mechanism, DREMFF extracts fine-grained features for each image from multiple salient regions as well as different scales. These captured information is capable of providing assistant decision-making at lower granularities. The mapped global features and the learned fine-grained features work collaboratively to enable viewpoint-robust person re-identification. Experiments on three challenging benchmarks consistently demonstrate the effectiveness of the proposed approach.
Harnessing System and Error Reporting in Windows

Microsoft Windows is an absolute treasure trove of information and data. There is literally nothing that happens on a PC without the event being recorded, and while this might sound a little scary from a data-privacy point of view (it’s not as this data is only ever stored locally on the PC), it’s just fantastic from an IT Support technician’s point of view.
Shapes and Their Approximate Descriptive Proximities

This chapter introduces a relaxed form of descriptive proximity, an approximation approach to determining the closeness of descriptions of nerve shapes which is highly application-oriented. It is seldom the case that a pair of cell complexes have matching descriptions, even though the particular cell complexes are close with the exception of one or more of the feature values in the descriptions of the complexes. This anomaly in descriptive proximities between cell complexes is prevalent in physical systems in spacetime, where cell complexes with matching descriptions are usually not found. For example, the wavelength of the reflected light from one triangulated surface shape $$\text{ sh }A$$ may be very close to the wavelength of the reflected light from triangulated surface shape $$\text{ sh }B$$ . If we choose wavelength the reflected light from a surface shape as a feature to consider, then the description $$\varPhi (\text{ sh }A)$$ would usually not equal $$\varPhi (\text{ sh }B)$$ . To circumvent this problem, approximate descriptive proximities are introduced in this chapter.
Beyond proficiency

Abstract Many studies have investigated the correlations between linguistic features and human judgements of writing quality. These studies usually investigate either proficient student writing or exceptional literary writing. The current study attempts to bridge these two perspectives by comparing proficient writing to award-winning exceptional writing using movie reviews written by bloggers and Pulitzer Prize winners. A range of linguistic features representing syntactic complexity, lexical complexity, and lexical cohesion were analyzed using both automated and interpretive methods. It is found that some, but not all, of the trends seen in writing development studies continue on to exceptional writing, with lexical sophistication and lexical cohesion through conceptual associations making the largest contributions to the differences between proficient and exceptional writers.
PREDICTION METHOD FOR READABILITY OF DOCUMENTS CORRESPONDING TO VARIOUS DESIGN LEVELS

When designing the environment used by an unspecified number of people based on the evaluation of users, it is necessary to plan reflecting the variation of the evaluation criteria of a large number of people and the attributes of the people assumed at designing. In the visual environment design of the room targeting a large number of people, it is necessary to give flexibility to the design by introducing various design levels such as an rating level and an evaluation probability. The rating level is the degree of visibility of the visual target and the evaluation probability is the probability that the rating level is achieved.
Agent-based Completion for Collecting Medical Note Parameters

In this paper, we present an agent-based completion that cooperates with a physician to optimize parameter-text pairs collection from a medical note. Currently, in hospitals, there is a medical coding process, which collects certain parameter-text pairs from the medical note's narrative text. The process starts with categorizing the text into certain parameter-text pairs. Then, the collected pairs are studied by the coders to produce correct medical codes. However, since the physicians may not aware of the coders' requirements, the medical coding process is quite problematic, such as lack of parameter-text pairs. To address this problem, we propose an agent-based completion, which represents the coder's view to categorize the text into certain parameter-text pairs and recommend the parameters to be filled. This paper shows a basic design of the agent and the background technologies to support the completion system.
Research on Security Architecture of Global Energy Internet Standard Support System

The global energy Internet standard support system plays a vital role in global energy interconnection. However, the system brings convenience to the global energy interconnection and introduces new security issues. Due to the diversity of users, the complexity of the environment and the internationalization, there will be more and more malicious network intrusions from outside and inside. The security system of the global energy Internet standard support system faces great challenges. To this end, this paper proposes a security architecture that combines active and passive, establishing a chain of trust between the active security architecture and the passive security architecture, ensuring a new security architecture, a security organization framework, a security policy framework, a security operation framework, and a closed-loop system that integrates the security technology framework. A deep learning algorithm was introduced for network intrusion learning to achieve sustainable management of the information security life cycle. The combination of active and passive security systems and continuous monitoring strategies based on deep learning fully ensure the security of the global energy Internet standard support system.
Distant Supervised Why-Question Generation with Passage Self-Matching Attention

Question generation (QG) aims to create a fluent question from a passage and a target answer. State-of-the-art approaches are mainly based on encoder-decoder models to generate questions from the given passage and answer, which focus on using the information contained in a particular part of the passage for QG, but unaware of the clues hidden in other parts of the passage. Besides, the existing work on QG mainly focus on generating factoid questions, which are less suitable for generating non-factoid questions such as why-questions. In this paper, we propose to augment encoder-decoder framework with a pair-wise self-matching attention mechanism to dynamically collect inter-sentential evidence from the whole passage according to the current passage word and answer information. Besides, to let the model be more suitable for why-question generation, we also involve some causal features in the encoding process. Finally, to tackle the lack of why-question generation training data problem, we adopt a distant supervised method with an initial causal knowledge base to generate a large training data for why-question generation. Extensive experiments on several data sets show that our model significantly outperforms state-of-the-art question generation models not only on why-question generation tasks, but also on other types of question generation tasks.