Reviewerly/exHarmony huggingface dataset:
Machine learning for tree structures in fake site detection

Tree data analysis has many applications in information security. In particular, HTML pages' DOM trees are an important target of analysis because web pages can be vectors for, and targets of, major cyberattacks like phishing. Previous attempts to incorporate tree data analysis into security applications, however, have been hampered by the lack of efficient methods for tree data analysis in machine learning. As such, most security research has focused on data representable as vectors of real numbers, like most machine learning work. Recent work, however, has yielded several efficiency break-throughs in tree analysis. One example is kernel methods, a methodological bridge that fills the gap between discretely-structured data (like trees) and multivariate analysis. Kernel methods enable applying a variety of multivariate analysis techniques such as SVM and PCA to trees. The method we are interested in is the subpath kernel. The subpath kernel offers the following advantages: (1) it is invariant over ordered and unordered trees; (2) it can be computed using an extremely fast linear-time algorithm compared to the quadratic time required to compute values of most tree kernels; (3) its excellent prediction accuracy has been proven through intensive experiments. This paper proposes a subpath kernel-based method for tree-structured security data. To demonstrate the effectiveness of our method, we apply it to the problem of detecting fake e-commerce sites, a sub-problem of phishing detection with a significant real-world financial cost. In an experiment on a real dataset of fake sites provided by a major e-commerce company, our method exhibited accuracy as high as 0.998 when training SVM with as few as 1,000 instances. Its generalization efficiency is also excellent: with only 100 training instances, the accuracy score reaches 0.996. While previous phishing detection methods relied on textual content, URL components, and blacklists, our approach is the first to leverage DOM trees, which makes it both more effective and more robust against adversarial attacks. Unlike URL or content changes, changing a page's DOM structure incurs large costs to criminals.
Augmented reality application in engineering education: N-Type MOSFET

In a period of rapid adaptation to technology in today’s world, limiting education to traditional methods leads to a rapid decline in the perception and interest of the learner. Augmented Reality (AR), which is among the most interesting technological innovation of the last years, are manifested in many sectors. It is used more widely in the field of education thanks to its superior advantages such as being portable, adapting to different devices/equipment, offering interactive digital content and arousing interest. In this study, AR application has been realized for the modeling of N-type MOSFET element, which is one of the basic courses of Electrical and Electronic Engineering and is widely used in analog and digital circuits. The contribution of the application to students’ perception of theoretical concepts was also examined. It concluded that AR made an important contribution to the concretization of abstract concepts and the developed educational material increased the motivation of the students.
Partially Observable Markov Decision Process for Monitoring Multilayer Wafer Fabrication

The properties of a learning-based system are particularly relevant to the process study of the unknown behavior of a system or environment. In the semiconductor industry, there is regularly a partially observable system in which the entire state of the process is not directly or fully visible due to uncertainties or disturbances. The model for studying such a system that permits uncertainties regarding the stochastic (Markov) process for state information acquisition is called a partially observable Markov decision process (POMDP). This study aims to deal with the optimization issue of compensation control bias of a dynamic multilayer lithography process in wafer fabrication with prior information, the existence of high-dimensionality, and unmeasurable uncertainties. We show how the POMDP on a linear state-space model with uncertainties can encode the information from past runs and layers, and deal with accumulated overlay error at the current run and layer. The Gibbs sampling is applied to optimize the belief function of POMDP optimization approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The multilayer overlay error of the photolithography process is one of the remarkable and challenging issues in wafer fabrication. In a multilevel manufacturing process, errors occur at each level, which would be accumulated in the upstream operations. The optimization objective will be even more critical in a high-mixed fabrication process. In this study, the learning-based control system emerged with the state-space model compensates the multilayer overlay error. The Gibbs sampling as a Bayesian approach as a core structure of optimization algorithm is utilized, which can be updated with information from engineering’s domain knowledge or estimated information about previous runs. The robustness of the proposed optimization algorithm is shown by comparing the distribution of overlay error with conventional methods and with a fast convergence rate of the learning algorithm.
Mandative subjunctive versus <i>should</i> in world Englishes: a new take on an old alternation

This study explores the alternation between the mandative subjunctive and its modal alternative with should across native and non-native Englishes. Methodologically, we try to improve on existing standards by investigating over 3,300 occurrences of the alternation from the Corpus of Web-based Global English and annotated for a range of linguistic factors analysed with a forest of conditional inference trees; also, we are exemplifying a new strategy for the use of random or conditional inference forests in corpus-based alternation studies. We obtain a forest with significant prediction accuracies and a good C-score and discuss the strongest predictors of the subjunctive versus should alternation across Englishes. Contrasting with existing research, our multi-factorial results: ( i) suggest that in British English the mandative subjunctive may not be dying out as much as we thought; and ( ii) individual suasive verbs influence speakers' use of the two variants more than their variety of English.
View Count of Online Videos Prediction Using Clustering View Count Patterns with Multivariate Linear Model

Social media of online video content should have some interesting applications, which provide convenience to users. For example, online advertising should be shown to the targeted users and the recommender system suggests appropriate videos to a user. Video view count prediction can help improve these recommendations because the video will be more interesting if it gets more view count. On YouTube, video view count and revenue are positively correlated. However, to correctly predict the view count of a video on YouTube, we divide problems into two issues. First, the view count has several patterns, which cause poor prediction. They should be grouped and trained specifically. Second, some patterns cause more errors in prediction because they are rare. Therefore, they should be separated to prevent overfitting.
Adjusting BERT’s Pooling Layer for Large-Scale Multi-Label Text Classification

In this paper, we present our experiments with BERT models in the task of Large-scale Multi-label Text Classification (LMTC). In the LMTC task, each text document can have multiple class labels, while the total number of classes is in the order of thousands. We propose a pooling layer architecture on top of BERT models, which improves the quality of classification by using information from the standard [CLS] token in combination with pooled sequence output. We demonstrate the improvements on Wikipedia datasets in three different languages using public pre-trained BERT models.
Question Answering on Scholarly Knowledge Graphs

Answering questions on scholarly knowledge comprising text and other artifacts is a vital part of any research life cycle. Querying scholarly knowledge and retrieving suitable answers is currently hardly possible due to the following primary reason: machine inactionable, ambiguous and unstructured content in publications. We present JarvisQA, a BERT based system to answer questions on tabular views of scholarly knowledge graphs. Such tables can be found in a variety of shapes in the scholarly literature (e.g., surveys, comparisons or results). Our system can retrieve direct answers to a variety of different questions asked on tabular data in articles. Furthermore, we present a preliminary dataset of related tables and a corresponding set of natural language questions. This dataset is used as a benchmark for our system and can be reused by others. Additionally, JarvisQA is evaluated on two datasets against other baselines and shows an improvement of two to three folds in performance compared to related methods.
Cloud Model-Based Comprehensive Evaluation Method for Entrepreneurs’ Uncertainty Tolerance

The evaluation of entrepreneurs’ uncertainty tolerance (UT) is more meaningful, e.g., predicting their behaviors, making psychological counseling strategies for them, etc. In fact, it is an uncertain problem that simultaneously contains randomness and fuzziness in evaluating entrepreneurs’ UT. Hence, it is difficult to solve it by traditional evaluation methods. This paper presents a cloud model-based comprehensive evaluation method for entrepreneurs’ UT which overcomes the inability of other methods to take into account randomness and fuzziness simultaneously. First, five UT levels are divided. Then, an evaluation index system which contains 14 secondary level indexes and four primary level indexes is constructed, and an analytic hierarchy process (AHP) method is used to obtain the weights for the secondary level indexes. Subsequently, the backward cloud generator (BCG) and virtual cloud model (CM) are used to obtain the CM of evaluation indexes, and cloud chart of evaluation results are generated by forward cloud generator (FCG). Finally, five core start-up founders of a high-tech company in China are chosen for a case study to illustrate our method. Compared with other traditional evaluation methods, our method has been verified to be a more competitive method. Its results are more visualized and low information lost.
Transfer Learning Layer Selection Using Genetic Algorithm

The performance of transfer learning in convolutional neural networks depends on the selection of which layers are to be learned again and which are not. Generally, layers selection is performed manually; however, as the number of layers increases, the layers selection process becomes increasingly difficult. Thus, we propose a method to select an effective layers in transfer learning automatically using a genetic algorithm. In the proposed method, a genotype representing which layers' weights are updated or fixed in transfer learning is considered, and we achieve efficient layers selection in the way that a genotype with high validation accuracy is survived during genotype selection. Experiments are performed using the InceptionV3 network that pre-trained ImageNet as source images with transfer learning to CIFAR-100 as target images. Experimental results demonstrate that the test data accuracy in an ensemble of models whose layers are selected by the genetic algorithm is 15% and 12% greater than that of models trained by from-scratch and fine-tuning, respectively. In general transfer learning approach, layers on the output side are selected as adjustable layers; however, it is found that the distribution of the selected layers as an effective adjustable layers obtained by the genetic algorithm extends to the entire network. Transfer learning using a genetic algorithm may successfully capture the characteristics of a convolutional neural network's structure.
Transformation of higher education: overcoming the conflict of competence and fundamentality

The purpose of the paper is to analyze the problems of transformation of higher education in Russia, expressed in the transition to a competence-based approach, which became a natural result of the introduction of the so-called “Bologna process”. The research methodology consists of a comparative taxonomic analysis of basic education and education obtained by competencies, with the identification of potential advantages and disadvantages. The introduction of new standards in the field of education, not of Russian origin, is considered by the authors as the result of defeat in institutional competition, which does not exceed arguments about the unity of diplomas or educational space. For the strategic development of Russia, it is necessary to build systems of higher education according to its own rules, focus on basic fundamental education, and consider the acquisition of certain competencies as an auxiliary action at the next stages of improving personnel of higher qualifications, when fundamental education has already been obtained. Thus, it is proposed to take the competence approach beyond the boundaries of university education to the stage of postgraduate professional development and additional training.
Secret Sharing Lower Bound: Either Reconstruction is Hard or Shares are Long

A secret sharing scheme allows a dealer to distribute shares of a secret among a set of n parties $$P=\{p_1,\dots ,p_n\}$$ such that any authorized subset of parties can reconstruct the secret, yet any unauthorized subset learns nothing about it. The family $$\mathcal {A} \subseteq 2^P$$ of all authorized subsets is called the access structure. Classic results show that if $$\mathcal {A}$$ contains precisely all subsets of cardinality at least t, then there exists a secret sharing scheme where the length of the shares is proportional to $$\lg n$$ bits plus the length of the secret. However, for general access structures, the best known upper bounds have shares of length exponential in n, whereas the strongest lower bound shows that the shares must have length at least $$n/\lg n$$ . Beimel conjectured that the exponential upper bound is tight, but proving it has so far resisted all attempts. In this paper we make progress towards proving the conjecture by showing that there exists an access structure $$\mathcal {A}$$ , such that any secret sharing scheme for $$\mathcal {A}$$ must have either exponential share length, or the function used for reconstructing the secret by authorized parties must have an exponentially long description. As an example corollary, we conclude that if one insists that authorized parties can reconstruct the secret via a constant fan-in boolean circuit of size polynomial in the share length, then there exists an access structure that requires a share length that is exponential in n.
Edge-preserving Filtering and Fuzzy Image Enhancement in Depth Images Captured by Realsense Cameras in Robotic Applications

This paper presents both the use of depth cameras in robotic applications and effects of post-processing on the captured depth images. The performance of depth cameras and post-process ...
Timestamp boson sampling

Quantum advantage, benchmarking the computational power of quantum machines outperforming all classical computers in a specific task, represents a crucial milestone in developing quantum computers and has been driving different physical implementations since the concept was proposed. A boson sampling machine, an analog quantum computer that only requires multiphoton interference and single-photon detection, is considered to be a promising candidate to reach this goal. However, the probabilistic nature of photon sources and the inevitable loss in evolution network make the execution time exponentially increasing with the problem size. Here, we propose and experimentally demonstrate a timestamp boson sampling scheme that can effectively reduce the execution time for any problem size. By developing a time-of-flight storage technique with a precision up to picosecond level, we are able to detect and record the complete time information of 30 individual modes out of a large-scale 3D photonic chip. We perform the three-photon injection and one external trigger experiment to demonstrate that the timestamp protocol works properly and effectively reduce the execution time. We further verify that timestamp boson sampler is distinguished from other samplers in the case of limited datasets through the three heralded single photons injection experiment. The timestamp protocol can speed up the sampling process, which can be widely applied in multiphoton experiments at low-sampling rate. The approach associated with newly exploited resource from time information can boost all the count-rate-limited experiments, suggesting an emerging field of timestamp quantum optics.
Imaging performance of fractal structuresparse aperture arrays

The angular resolution of optical system is limited by the ratio of the wavelength to the aperture of the entrance pupil, indicating that the optical system with large aperture has a high spatial resolution. Sparse aperture imaging is one of the effective solutions to the problem that the telescope is bulky, heavy and difficult to manufacture. According to the self-similarity and multi-scale characteristics of fractal configuration, we propose a sparse aperture array and analyze its performance for synthetic aperture imaging system. In the array Golay-3 is used as a structural unit to expand a multi-layered fractal configuration in a self-similar manner. Given the analytical expression of the pupil function which is reduced by dimensionless parameters, we calculate the modulation transfer functions (MTFs), the practical cut-off frequencies and the middle spatial frequency characteristics of the fractal configuration under different fill factors and different outer layer rotational angles. We analyze both the MTF values and the performance parameters of the fractal structure for the cases of &lt;i&gt;N&lt;/i&gt; = 3, 9, and 18, respectively. The results show that the decrease of fill factor does not significantly change the MTF curve nor the practical cutoff frequency in a range of fill factor between 0.0952 and 0.2246. The outer layer rotational angle has a periodicity, and the change in the angle has no large influence on the practical cutoff frequency. When the reduced aperture parameter is &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ {d_0} = 1$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="19-20190818_M3.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="19-20190818_M3.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; and the fill factor is 22.46%, the middle spatial frequency of &lt;i&gt;N&lt;/i&gt; = 18 array is more stable and the practical cut-off frequency is higher. Using the fractal self-similarity, the aperture of the system can be expanded effectively while maintaining the middle spatial frequency characteristics. The computing results are of scale invariance due to the adoption of the reduced aperture parameter.
Low-light-level image enhancement based on fusion and Retinex

Image enhancement, which aims at improving the contrast of low-light-level image and shows the details in the dark, is a widely used technology. When traditional multi-scale retinex (MSR) is used for low-light-level image enhancement, enhancement result will be over-enhanced and the so-called halo will occur. In order to overcome this shortcoming, the result of MSR needs to be further processed. First, MSR result and low-light-level image are fused with weight related to the grey value of the pixels. The result shows that the halo is significantly suppressed, but the brightness of the image is low. Thus, we fuse the image again and take the fusion image which fuses the first step result with the MSR result as the final result. Experimental results show that the proposed algorithm can suppress the halo and preserve the details.
Depth-Adapted CNN for RGB-D Cameras

Conventional 2D Convolutional Neural Networks (CNN) extract features from an input image by applying linear filters. These filters compute the spatial coherence by weighting the photometric information on a fixed neighborhood without taking into account the geometric information. We tackle the problem of improving the classical RGB CNN methods by using the depth information provided by the RGB-D cameras. State-of-the-art approaches use depth as an additional channel or image (HHA) or pass from 2D CNN to 3D CNN. This paper proposes a novel and generic procedure to articulate both photometric and geometric information in CNN architecture. The depth data is represented as a 2D offset to adapt spatial sampling locations. The new model presented is invariant to scale and rotation around the X and the Y axis of the camera coordinate system. Moreover, when depth data is constant, our model is equivalent to a regular CNN. Experiments of benchmarks validate the effectiveness of our model.
Onboarding Bot for Newcomers to Software Engineering

Software development teams dedicate considerable resources to training newcomers. Newcomers are new developers to a software project. The software onboarding process is more complicated than onboarding into other organizations. It is much more challenging and time-consuming. The role of a mentor in onboarding newcomers in software engineering is well understood. However, the disruptions to the work of an experienced developer can reduce the quality of their work and job satisfaction. We propose a conversational bot that can help onboard newcomers to a software project instead of an experienced programmer. The bot will act as a mentor for the newcomer, thus putting less stress on experienced programmers. The bot will also be able to scan outside sources, such as stack overflow, for solutions to issues a newcomer may face. The newcomer will be able to interact with the bot using natural language. We will use this bot to assess improvements to code quality in future studies.
In-situ cleaning process of chamber filter presses with sensor-controlled and demand-oriented automation

The content of this paper is the cleaning of filter surfaces from remaining filter cakes. Remaining dirt on filter surfaces is a widespread problem. To counteract this, increasingly automated cleaning systems are being integrated, which clean these surfaces with great cleaning effort. The reduction of the cleaning effort can be achieved by the combination of a camera with a surface contamination detection software. The advantages for the cleaning process are a more efficient cleaning, less produced wastewater and an effective documentation of the cleaning. The cleaning procedure is carried out using 3 steps: It starts with the recording of a picture. Then, an algorithm detects contamination, generates corresponding coordinates and a nozzle path. These coordinates are then approached by means of a xyz table and the contamination is cleaned by a jet stream. Different nozzles and cleaning parameters were tested and their influence on the cleaning effectiveness is discussed in the context of this study.