Reviewerly/exHarmony huggingface dataset:
No Time for Downtime: Understanding Post-Attack Behaviors by Customers of Managed DNS Providers

We leverage large-scale DNS measurement data on authoritative name servers to study the reactions of domain owners affected by the 2016 DDoS attack on Dyn. We use industry sources of information about domain names to study the influence of factors such as industry sector and website popularity on the willingness of domain managers to invest in high availability of online services. Specifically, we correlate business characteristics of domain owners with their resilience strategies in the wake of DoS attacks affecting their domains. Our analysis revealed correlations between two properties of domains - industry sector and popularity - and post-attack strategies. Specifically, owners of more popular domains were more likely to re-act to increase the diversity of their authoritative DNS service for their domains. Similarly, domains in certain industry sectors were more likely to seek out such diversity in their DNS service. For example, domains categorized as General News were nearly 6 times more likely to re-act than domains categorized as Internet Services. Our results can inform managed DNS and other network service providers regarding the potential impact of downtime on their customer portfolio.
Development of Application for Practicing High School Math Problems

The aim of this study is developing an effective web application for training the academic ability of high school education using e-learning. E-learning has some features, e.g., the service with e-learning can be used anytime and anywhere, learning data can be recorded and analyzed easily. By adopting these advantages, a web application for practicing high school math was developed in this study. The application’s main functions are generating math problems dynamically and providing thousands types of problems for users practicing. These functions enable users to train their math ability effectively. Besides, user-friendly user interface (UI) was designed for the usage of various types of digital devices. Besides, a survey on trial of this application was conducted. A lot of suggestions and comments especially for the UI design from the participants in this survey were obtained.
HTS-DL: Hybrid Text Summarization System using Deep Learning

Abstractive text summarization is the task of creating a summary from a document by merging facts from different sources and make a short description of them. In this procedure, the meaning and the content information should be kept. In this paper, a hybrid summarization system using deep recurrent neural network is proposed, which can create new sentences by information extracted from the text The proposed model is the combination of extractive and abstractive summarization and has the encoder-decoder structure. The encoder extracts information from the source document and encodes this information in a compressed representation. The decoder takes the encoder’s output as input and generates a summary, which has an acceptable semantic and syntactic structure. Experimental results show that the proposed model could improve both the performance of abstractive summarization and the time of training. This model does the single-document multi-sentence summarization and does not have any dependency on language. Therefore, it can be used for other languages without any modification in future.
TermoPL

We present a new version of the terminology extraction tool - TermoPL. This version not only allows the ranking of term candidates but also their semantic grouping. To ensure the results are precise, we use the WordNet lexical database for identifying semantic relations between words. The tool was designed primarily for Polish texts, but the current version is tagset-independent and can be adapted to process texts in other languages. The new semantic grouping feature has been fully implemented for Polish texts, but we plan to make it available for English texts as well.
Hardwood Grain Image Restoration and Enhancement via Gaussian Histogram Specification and Adaptive Color Adjustment

Hardwood is widely used in the surface decoration of furniture and wood products due to its rich texture and durable surface, and the improvement of wood grain images is vital to promote the aesthetics of wood surfaces. In order to restore the Gaussian distribution of distorted wood grain images and reproduce a sharp and clear wood surface, a Gaussian histogram specification algorithm based on the constant mean and variance values of red (R), green (G), and blue (B), and an adaptive color adjustment algorithm based on the color extension of R, G, and B histograms was proposed, respectively. Objective evaluation methods of histogram distribution, colorfulness index, contrast index, and sharpness index were used independently to evaluate the visual effect of the images processed by the two algorithms. Objective and subjective evaluation results showed that although the Gaussian method had only a small influence on the visual effect of hardwood grain images, it could restore the distorted images by repairing the irregular color points to weaken the adverse impact on visual impression. Meanwhile, extra attention should be paid to the processing of images with prominent uneven color transitions, because the Gaussian method might have an imperceptible smoothing or enhancing effect. The adaptive color adjustment method had a favorable enhancement effect on most hardwood grain images. However, the color extension coefficients of the over-enhanced images should be reduced to eliminate overcompensation and color shift. Compared with the traditional enhancement method unsharp mask (USM) and the methods designed for sand-degraded images and underwater images, the proposed adaptive color adjustment at the 1.5 coefficient could effectively enhance the images from the perspective of wood grain visibility and color retention.
An Intelligent Perception Model and Parameters Adjust Method for Quality of Experience

With the coming of the 5G network era, Quality of Experience (QoE), which is used to describe experience quality of customers in the Direct Connect (DC) network, is critical to operators and hence has been widely studied. The existing evaluation method for QoE is unable to dynamically adjust QoE parameters so there will be deviations to reflect the real experience of customers when complaint events occur. In this paper, a novel progressive QoE evaluation model for network products is formulated. Subsequently, considering the customer complaints occurrences which represent that the real customer experience and previously calculated QoE may mismatch, the rules based on application types to judge whether QoE parameters needs to be adjusted are made. Then, a dynamic adjustment model of QoE parameters is further developed to reflect complaint customer experience. Finally, the obtained experimental result verified that the proposed dynamic perception method for QoE can accurately characterize the real customer experience. The evaluation result for QoE model based on customer satisfaction questionnaires proves the effectiveness of the QoE-aware model. The evaluation result for adjusted QoE model based on business complaints shows that the adjusted QoE calculation variance reduction rate exceeds more than 50%, which indicates that the proposed method has better ability to reflect the real customer experience.
3D Simulation Design and Application of Traditional Hanfu Based on Internet of Things

Chinese traditional Hanfu is a kind of clothing that can reflect the changes of Chinese history, culture, and dynasties. With the improvement of people’s aesthetic ability and the pursuit of national culture, Hanfu has shown a state of revival. The study of Hanfu has become a research hotspot in today’s era. However, there is a big difference between the design of traditional Hanfu and the design of modern clothing. It not only needs to consider people aesthetics and preferences but it also needs to further consider the historical and cultural information represented by traditional Chinese Hanfu. This is a more critical and difficult point for Hanfu designers. If Hanfu cannot be well combined with history and culture, this will easily lead to a misinterpretation of Hanfu. In this study, the feasibility of the 3D simulation design of Hanfu was fully studied by combining the Internet of Things technology and the convolutional neural network method. The research results show that the Internet of Things technology can efficiently and accurately collect the characteristics of patterns, colors, shapes, and historical information of Hanfu. The reliability of IoT technology also improves the accuracy of CNN methods in predicting Hanfu eigenvalues. The largest prediction error is only 2.84%. CNN can also well capture the relationship between historical information features of Hanfu and dynasties, and all predicted feature values are within the 95% confidence interval.
EVASS: Emotional Variational End-to-End Speech Synthesis with Semi-Supervised and Adverserial Learning

Communicating one's inner state - their emotions and feelings - forms one of the core principles of social communication and behavior in humans. Emotion is an important component of speech, and its inclusion in synthetic speech will allow for breakthroughs in applications like human-machine interfacing, e-book reading, and voice acting. However, modelling emotions in speech in an end-to-end manner has so far remained an under-explored topic of research. To address this, we experiment with novel methods in global emotional modelling in unsupervised, semi-supervised and adverserial contexts using an end-to-end text-to-speech (TTS) architecture. We condition the latent space, duration prediction and audio generation on novel hybrid labels based on ground truth data – 14 emotion labels, 64 sentiment analysis labels, and speaker labels - which may be inferred from input text during inference. Experiments on conditional discriminators were also performed. The final proposed model produces high quality expressive results comparable to the state of the art.
The Simulated Body: A Preliminary Investigation into the Relationship Between Neuroscientific Studies, Phenomenology and Virtual Reality

Abstract The author of this paper discusses the theme of the "simulated body", that is the sense of "being there” in a body that is not one's own, or that does not exist in the way one perceives it. He addresses this issue by comparing Immersive Virtual Reality technology, the phenomenological approach, and Gerald Edelman's theory of Neural Darwinism. Virtual Reality has been used to throw light on some phenomena that cannot be studied experimentally in real life, and the results of its simulations enrich the phenomenological discourse on the lived body. Virtual “Reality” seems to replicate—at least in part—the simulation mechanisms of our mind, thus favoring developments in the field of philosophy of mind.
On the Suitability of Bagging-Based Ensembles with Borderline Label Noise

Real-world classification data usually contain noise, which can affect the accuracy of the models and their complexity. In this context, an interesting approach to reduce the effects of noise is building ensembles of classifiers, which traditionally have been credited with the ability to tackle difficult problems. Among the alternatives to build ensembles with noisy data, bagging has shown some potential in the specialized literature. However, existing works in this field are limited and only focus on the study of noise based on a random mislabeling, which is unlikely to occur in real-world applications. Recent research shows that other types of noise, such as that occurring at class boundaries, are more common and challenging for classification algorithms. This paper delves into the analysis of the usage of bagging techniques in these complex problems, in which noise affects the decision boundaries among classes. In order to investigate whether bagging is able to reduce the impact of borderline noise, an experimental study is carried out considering a large number of datasets with different noise levels, and several noise models and classification algorithms. The results obtained reflect that bagging obtains a better accuracy and robustness than the individual models with this complex type of noise. The highest improvements in average accuracy are around 2–4% and are generally found at medium-high noise levels (from 15–20% onwards). The partial consideration of noisy samples when creating the subsamples from the original training set in bagging can make it so that only some parts of the decision boundaries among classes are impaired when building each model, reducing the impact of noise in the global system.
Collaborative Collection of Multilingual Pronoun Substitutes and Address Terms

This paper describes the encoding scheme for pronoun substitutes and address terms in eight Asian languages based on the vocative studies. The target languages are selected according to the availability of the language experts and resources. The nature of pronoun substitutes and address terms expression across the languages can be confirmed by the concepts defined in the WordNet. In this study, a workbench for text data collection (WordList) has been carefully designed to maintain the input data consistency and the semantic linkage between the target languages. The WordList is a web-based application facilitating an online collaborative data input. It maintains the data in MongoDB, and supports JSON and CSV format file exporting for database backup and batch data cleansing for further expression pattern study.
Explainable Forecasts of Disruptive Events using Recurrent Neural Networks

This paper describes the Crystal Cube method we developed for forecasting disruptive events around the world, specifically Irregular Leadership Change. Crystal Cube uses a Recurrent Neural Network (RNN) with Long-Short Term Memory (LSTM) units for forecasting. In this paper special emphasis is put on explanations of the network forecasts. We are using SHapley Additive exPlanations (SHAP) for individual forecast explanations and we are aggregating the explanations separately for True Positives, False Positives, True Negatives, and False Negatives. The method can be extended to Deep Reinforcement Learning models for self-driving cars or unmanned fighter jets.
Tuxedo

Proof-of-Work~(PoW) based blockchains typically allocate only a tiny fraction (e.g., less than 1% for Ethereum) of the average interarrival time~$\mathbbI $ between blocks for validating smart contracts present in transactions. In such systems, block validation and PoW mining are typically performed sequentially, the former by CPUs and the latter by ASICs. A trivial increase in validation time~$(τ)$ introduces the popularly known Verifier's Dilemma, and as we demonstrate, causes more forking and hurts fairness. Large τ also reduces the tolerance for safety against a Byzantine adversary. Solutions that offload validation to a set of non-chain nodes (a.k.a. off-chain approaches) suffer from trust and performance issues that are non-trivial to resolve.
Role Playing as a Method of Learning in Knowledge Engineering

Role-play promotes a deeper understanding of the methods of building knowledge-based systems. Students are trained to interview experts to extract knowledge for intelligent systems and, at the same time, to argue their opinion. Role-play allows you to master the necessary skills of a knowledge engineer (cognitologist). Parsing errors allows you to point out opportunities that were not used when extracting knowledge. The role-playing game can involve not only two participants (student-"cognitologist" and student-"expert", but all members of the student group. The teacher has ample opportunities to adjust the course of the role-playing game.
In their own words: Perspectives on collection weeding from library employees and teaching faculty

Following a Science and Engineering collection weeding project of over 350,000 print items, our research team conducted interviews with 20 library employees and 19 teaching faculty involved in the project. The purpose of the interviews was to assess the interviewees' perspectives and feelings relating to the time required to complete the project, inter- and intra- library communication, the decision-making processes, and their personal assessment of the costs and benefits of the project. The interviewees also offered their views of the overall project's successes and areas for improvement. Drawing from a qualitative analysis of the interviews, we provide guidance for other academic libraries preparing to undertake their own collection weeding projects including suggestions for what to expect and what to manage throughout the process.
Market‐inspired framework for securing assets in cloud computing environments

Abstract Self‐adaptive security methods have been extensively leveraged for securing software systems and users from runtime threats in online and elastic environments, such as the cloud. The existing solutions treat security as an aggregated quality by enforcing “one service for all” without considering the explicit security requirements of each asset or the costs associated with security. Dealing with the security of assets in ultra‐large environments calls for rethinking the way we select and compose services—considering not only the services but the underlying supporting computational resources in the process. We motivate the need for an asset‐centric, self‐adaptive security framework that selects and allocates services and underlying resources in the cloud. The solution leverages learning algorithms and market‐inspired approaches to dynamically manage changes in the runtime security goals/requirements of assets with the provision of suitable services and resources, while catering for monetary and computational constraints. The proposed framework aims to inform the self‐adaptive security efforts of security researchers and practitioners operating in dynamic large‐scale environments, such as the Cloud. To illustrate the utility of the proposed framework it is evaluated using simulation on an application based scenario, involving cloud‐based storage and security services.
Multisource Target Data Fusion Tracking Method for Heterogeneous Network Based on Data Mining

This research is on heterogeneous network fusion method of multisource target data based on data mining. Firstly, it is a distributed storage structure model for building heterogeneous network multisource target data. Then, using the phase space reconstruction method, a grid distribution structure model for data fusion tracking is constructed, and realize visual scheduling and automatic monitoring of multisource target data. Finally, according to the feature extraction results, analyze the statistical characteristics of multisource target data in heterogeneous networks, combined with the fuzzy tomographic analysis method, multilevel fusion, and adaptive mining of multisource target data, extract the associated feature quantities in it, and realize the fusion tracking of data. The simulation results show that, in relatively simple heterogeneous networks, the feature mining error of the proposed method is nearly 2.11% lower than the two traditional methods. In relatively complex heterogeneous networks, the feature mining error of the proposed method is nearly 6.48% lower than the two traditional methods. It can be seen that this method has better adaptability for fusion tracking of heterogeneous network multisource target data, the anti-interference ability is strong, and the tracking accuracy in the data fusion tracking process is also improved.
Research on Repetition Counting Method Based on Complex Action Label String

Smart factories have real-time demands for the statistics of productivity to meet the needs of quick reaction capabilities. To solve this problem, a counting method based on our decomposition strategy of actions was proposed for complex actions. Our method needs to decompose complex actions into several essential actions and define a label string for each complex action according to the sequence of the essential actions. While counting, we firstly employ an online action recognition algorithm to transform video frames into label numbers, which will be stored in a result queue. Then, the label strings are searched for their results in queue. If the search succeeds, a complex action will be considered to have occurred. Meanwhile, the corresponding counter should be updated to accomplish counting. The comparison test results in a video dataset of workers’ repetitive movements in package printing production lines and illustrate that our method has a lower counting errors, MAE (mean absolute error) less than 5% as well as an OBOA (off-by-one accuracy) more than 90%. Moreover, to enhance the adaptability of the action recognition model to deal with the change of action duration, we propose an adaptive parameter module based on the Kalman filter, which improves counting performances to a certain extent. The conclusions are that our method can achieve high counting performance, and the adaptive parameter module can further improve performances.
INmfCA Algorithm for Training of Nonparallel Voice Conversion Systems Based on Non-Negative Matrix Factorization

In this paper, we propose a new training framework named the INmfCA algorithm for nonparallel voice conversion (VC) systems. To train conversion models, traditional VC frameworks require parallel corpora, in which source and target speakers utter the same linguistic contents. Although the frameworks have achieved high-quality VC, they are not applicable in situations where parallel corpora are unavailable. To acquire conversion models without parallel corpora, nonparallel methods are widely studied. Although the frameworks achieve VC under nonparallel conditions, they tend to require huge background knowledge or many training utterances. This is because of difficulty in disentangling linguistic and speaker information without a large amount of data. In this work, we tackle this problem by exploiting NMF, which can factorize acoustic features into time-variant and time-invariant components in an unsupervised manner. The method acquires alignment between the acoustic features of a source speaker's utterances and a target dictionary and uses the obtained alignment as activation of NMF to train the source speaker's dictionary without parallel corpora. The acquisition method is based on the INCA algorithm, which obtains the alignment of nonparallel corpora. In contrast to the INCA algorithm, the alignment is not restricted to observed samples, and thus the proposed method can efficiently utilize small nonparallel corpora. The results of subjective experiments show that the combination of the proposed algorithm and the INCA algorithm outperformed not only an INCA-based nonparallel framework but also CycleGAN-VC, which performs nonparallel VC without any additional training data. The results also indicate that a one-shot VC framework, which does not need to train source speakers, can be constructed on the basis of the proposed method.
Developing Information Extraction System for Disaster Impact Factor Retrieval from Web News Data

Natural and man-made disasters have had a long-term impact on our country, resulting in significant loss of life and economic benefits. India’s disaster vulnerability has changed over time, emphasizing the importance of a holistic approach to disaster management. When assessing a disaster, it is important to look past the intensity scales and go deeper into the impact factors, which can help in post-disaster activities. Gathering data on hazards has become difficult due to the proliferation of knowledge available on the Internet. Using conventional natural language processing methodologies, the proposed work aims to derive data related to hazard impact factors from online news sources. Approximately, 80% of the impact factor information was retrieved effortlessly from about 200 news articles. The derived data can be used to create a severity scale that incorporates impact factors as well as intensity scales, allowing for better hazard analysis.