Reviewerly/exHarmony huggingface dataset:
Anomaly Detection with Partially Observed Anomaly Types

In this paper, we consider the problem of anomaly detection when a small number of anomaly types are observed. Previous research primarily focused on supervised learning, when all samples are labeled. And unsupervised learning is used, when all samples are unlabeled. However, many settings do not satisfy the above two situations. Recently, there are some studies on the situations that anomalies are partially observed (e.g., Anomaly Detection with partially Observed Anomalies). It is generally believed that the anomalies are classifiable in these studies. And it is common that the types of observed anomalies cannot include all types of anomalies in the case of partially observed anomalies. We refer to this problem as anomaly detection with partially observed anomaly types and propose a two-stage anomaly detection algorithm in this condition. The proposed method in this paper is based on Anomaly Detection with partially Observed Anomalies and is available in the new setting. Experimental results demonstrate the effectiveness of the proposed method both in the case of insufficient types of observed anomalies and in the case of sufficient types of observed anomalies. Besides, anomaly detection with partially observed anomaly types avoids the use of hyper-parameter and has high generality in different datasets.
Instagram Mobile Application Digital Forensics

In this research, we developed a plugin for our automated digital forensics framework to extract and preserve the evidence from the Android and the IOS-based mobile phone application, Instagram.This plugin extracts personal details from Instagram users, e.g., name, user name, mobile number, ID, direct text or audio, video, and picture messages exchanged between different Instagram users.While developing the plugin, we identified resources available in both Android and IOS-based devices holding key forensics artifacts.We highlighted the poor privacy scheme employed by Instagram.This work, has shown how the sensitive data posted in the Instagram mobile application can easily be reconstructed, and how the traces, as well as the URL links of visual messages, can be used to access the privacy of any Instagram user without any critical credential verification.We also employed the anti-forensics method on the Instagram Android's application and were able to restore the application from the altered or corrupted database file, which any criminal mind can use to set up or trap someone else.The outcome of this research is a plugin for our digital forensics ready framework software which could be used by law enforcement and regulatory agencies to reconstruct the digital evidence available in the Instagram mobile application directories on both Android and IOS-based mobile phones.
The development of a dance-musification model with the use of machine learning techniques under COVID-19 restrictions

Interactive technologies enable dancers to control the music in real-time with their movement. This paper presents the design and development of a model which takes as input a dancer's movement and outputs music, structurally related to dance, with the use of machine learning techniques. Both the technical and artistic aspects of the model development are described in detail. In particular, the paper compares the use of machine learning techniques to traditional coding, in interactive dance and music applications. Moreover, it describes the significant discrimination between movement sonification and dance musification and explains why the model presented here falls into the second category. Special focus is given to the implications of the COVID-19 restrictions regarding the established collaboration with the dancer.
Multi-modal sensing positioning technology for intelligent verification of stacked kilogram weights

Unmanned verification of weights requires machines to grab and carry weights on behalf of people. High-precision spatial coordinates of weights are the key parameters for unmanned verification of weights. This article starts with multimodal sensing positioning technology, and proposes a spatial positioning technology solution based on multi-modal sensing stacking kilogram weights. Through technical solutions such as deep learning, LM optimization, and least squares fitting, the stacking of kilograms under fluorescent lighting is realized. The spatial positioning of the weight handle forms a versatile method for positioning the handles of stacked kilogram weights. Experiments were carried out by simulating the construction of 3×3×2 stacked kilogram weights in different states. The results showed that spatial positioning accuracy of the stacked weights instance is millimeter-level, and the calculation time of the overall algorithm is less than 6s, which can realize the spatial positioning of stacking kilogram weights in complex environments.
3-D Epanechnikov Mixture Regression in integral imaging compression

Integral imaging is a kind of 3D display with no glasses, which represents the future developments. Elementary image array (EIA) is an essential component of integral imaging. Our coding framework includes pre-processing, modeling, and reconstruction. We acquire the sub-EIA from the original EIA and get the offsets between adjacent elementary images (EIs) through pre-processing. As for modeling, we get the optimal combination of 3-D Epanechnikov Mixture Regression (3-D EMR) or 3-D Gaussian Mixture Regression (3-D GMR) by Elementary Image Adaptive Model Selection (EI-AMLS) algorithm to achieve the best modeling of sub-EIA. Finally, the linear-based reconstruction is completed according to the correlation between adjacent EIs. Our decoded images realize a clearer outline reconstruction and more superior coding efficiency than HEVC and JPEG2000 below about 0.05bpp. Furthermore, the proposed method can achieve the same visual effect as HEVC with only 15% to 80% time consumed.
The effect of time drift in source code authorship attribution

Stylochronometry deals with the influence of time in an author's style, specifically how it changes stylometric features. Analysis of time drift occurrence is important especially for a dataset creation process of other works in this area. In this paper, we performed experiments using the Google Code Jam dataset to show the influence of time drift in the area of source code authorship attribution. Our experiments revealed that there is significant time drift in stylometric features in one year difference, which is enlargening as the difference of time increases. Another interesting result is that when training our authorship attribution method on data from the future and testing on data from the past, the time drift is lower than in opposite direction. Also, we found the relation between the length of source code and the accuracy of our authorship attribution method.
Enhancing Large Batch Size Training of Deep Models for Remote Sensing Applications

A wide variety of Remote Sensing (RS) missions are continuously acquiring a large volume of data every day. The availability of large datasets has propelled Deep Learning (DL) methods also in the RS domain. Convolutional Neural Networks (CNNs) have become the state of the art when tackling the classification of images, however the process of training is time consuming. In this work we exploit the Layer-wise Adaptive Moments optimizer for Batch training (LAMB) optimizer to use large batch size training on High-Performance Computing (HPC) systems. With the use of LAMB combined with learning rate scheduling and warm-up strategies, the experimental results on RS data classification demonstrate that a ResNet50 can be trained faster with batch sizes up to 32K.
Sarcastic Text Detection Using Keras

Sarcastic text is a type of text that contains a kind of irony, in which negative expressions are expressed as positive by attributing meanings to words that contradict their real meanings during communication.During face-to-face communication, changes in tone of voice, body language, eye contact or word stress make it easier for the other person to detect the sarcastic expression.However, it is challenging to detect sarcastic expressions only through text in machine learning-based systems since human-centred qualities cannot be transferred.Newspapers often use sarcastic expressions in their headlines to attract people's attention.However, many people cannot fully understand whether these expressions are sarcastic or not without reading the content of the text.As a result, they can transmit false information to the people around them through direct communication or social media.In this study, to prevent such misinformation, newspaper headlines with and without sarcasm are tried to be classified on three different GPUs with deep learning methods.As a result, the developed model can successfully detect news headlines containing sarcasm.
Remote Sensing Image Enhancement by Rolling Guidance and Hazy Image Model

An efficient image enhancement method should improve the contrast in the image while keeping the edge and color information. Since existing approaches seem not to fulfill all these demands, a hybrid approach which will combine advantages of individual approaches is proposed in this work. The multiscale bilateral filter is replaced by an iterative joint version where the output is used as guidance image for the next iterations. Then a multiscale structure is designed by the appropriate modifications of the spatial and range kernels as in the multiscale bilateral filter. A final refining is performed by the local use of the Hazy Image Model based method (HIM) on the resulting image. Visual and quantitative comparisons with conventional Discrete Wavelet Transform and Singular Value Decomposition based method (DWT-SVD), Regularized Histogram Equalization with Discrete Cosine Transform method (RHE-DCT), Bilateral Filtering based method (BF), and HIM method demonstrate the superiority of the proposed method and the resulting hybrid method for remote sensing image enhancement.
Tangible vs. Multi-Touch: Comparing Potential to Enhance Learning for Preschool Children Using Eye-tracking

Studies have suggested the possible advantages of Tangible User Interfaces (TUIs) over Multi-Touch Interfaces (MTIs) in preschool education, but more objective evidence is lacking. In this study, we designed an math game called “THE NUMBERS” for preschool children, using eye-tracking to assess cognitive differences between the TUI version and the MTI version to compare their potential to enhance learning. Results showed that when using the TUI version participants made more attempts, which was a significant predictor of learning outcomes; the TUI version had a lower cognitive load; participants' attention was more focused on areas containing critical content using the TUI version; and the TUI version had higher satisfaction than the MTI version because it was more accessible. These findings provided theoretical supports for the use of TUIs in preschool education.
Detecting Gaussian entanglement via local quantities

Entanglement detection and quantification usually require the knowledge of correlations and thus is a global issue. For both theoretical and practical reasons, it is desirable to characterize entanglement via local quantities. Of course, this can only be achieved in special setups with some underlying structure encoding the correlations. For the often encountered bilinear interactions such as beamsplitter experiments, we propose an operational and friendly entanglement criterion for the two-mode Gaussian output states. Specifically, we show that in order to detect and evaluate the entanglement of the output states of a bilinear interaction involving uncorrelated Gaussian input states, we do not need to know the joint covariance matrix of the whole two-mode output states. Instead, we only need the respective covariances of the reduced states of the two input modes and that of the two output modes, which are all local quantities pertaining to a single mode and can be easily measured locally without explicitly considering the correlations between the modes. This is achieved by exploiting the input-output characteristics of bilinear interactions. The result recasts the celebrated entanglement criteria of Simon (Phys. Rev. Lett. , 84 (2000) 2726) and Duan et al . (Phys. Rev. Lett. , 84 (2000) 2722) into a more convenient and simple form.
Exploring register with<i>The Prime Machine</i>

Abstract Corpus approaches underpin a range of postgraduate studies and professional work in language, linguistics, translation and beyond. Awareness of the influences of contextual features on language choice is important for many activities: exploring new text varieties; finding relationships between social factors and language patterning; considering choices for post-editing machine translation; and understanding the very nature of language. Work on register relies on corpus methods, but more support and direction could be offered to help undergraduates gain earlier insights into the power of such corpus analysis. This paper introduces some ways register differences can be revealed through The Prime Machine corpus tool ( Jeaco 2017a ) and describes the design of a practically-oriented undergraduate module which uses this concordancer. Software features include the organization of texts and presentation of source information for readymade corpora, and methods which can be used to reveal useful starting points for register analysis of do-it-yourself corpora.
Feedback linearly extended discrete functions

We study a new flexible method to extend linearly the graph of a non-linear, and usually not bijective, function so that the resulting extension is a bijection. Our motivation comes from cryptography. Examples from symmetric cryptography are given as how the extension was used implicitly in the construction of some well-known block ciphers. The method heavily relies on ideas brought from linear coding theory and secret sharing. We are interested in the behaviour of the composition of many extensions, and especially the space of parameters that defines a family of equations based on finite differences or linear forms. For any linear extension, we characterize entirely the space of parameters for which such equations are solvable in terms of the space of parameters that render those equations for the corresponding non-linear extended functions solvable. Conditions are derived to assess the solvability of those kind of equations in terms of the number of compositions or iterations. We prove a relation between the number of compositions and the dimensions of vector spaces that appear in our results. The proofs of those properties rely mostly on tools from linear algebra.
Influence of personal value orientations on the successful professional development undergraduates of a Pedagogical University

The article examines the features of the implementation of the value orientations of the individual when receiving pedagogical education in modern conditions, studies the influence of stable value orientations on the successful professional development of a master’s student at a pedagogical university. The results of the study of personal value orientations are presented by the authors on the basis of diagnostics of the real structure of personal value orientations according to the method of S.S. Bubnova. Level determination of value orientations development of Omsk State Pedagogical University undergraduates made it possible to identify the motivational field of students, the level of development of self-organization, the direction of building long-term professional goals, the development of reflexive skills and time management skills. The article also formulates the directions for improving the training of pedagogical personnel, taking into account the value orientations of undergraduates.
Deep Regressor Networks for Blind Image Deblurring

Image restoration concerns mainly smoothing noise and de-blurring images that were corrupted either during acquisition or transmission. Since traditional deconvolution filters are highly dependent on specific kernels or prior knowledge to guide the deblurring process, image blur classification and further parameter estimation are critical for blind image de-blurring. This paper tackles the problem in three steps: (i) it first identifies the blur type for each input image, (ii) then it estimates the respective kernel parameter, and (iii) finally, it uses deconvolution filters to restore the blurred image. The proposed approach, called Deep Regressor Networks, showed promising results in general-purpose and remote sensing image datasets corrupted by different types and blur levels than some state-of-the-art techniques.
Drivers of E-Business Value Creation in Banking Sector in Jordan: A Structural Equation Modeling Application

With the development and growth of internet, its applications of e-banking, e-commerce, and e-business became irreplaceable channels regarding its fast access, rich content, and smooth interactivity. High investments are paid toward improving the quality of service offered by the banks. This paper is dedicated to empirically investigating the drivers of e-Business value creation in the Jordanian banking sector. This work summarizes the main differences among employees of Jordanian and foreign bank regarding their perspectives. Many of the competing foreign banks to the Jordanian banks are enforced with huge financial capital, having long periods of banking practices and are employing cutting-edge technologies and tools. To minimize the technological gap, Jordanian banks are working hard to develop their e-Business services. This in one hand has to enhance their trust, satisfaction, and commitment toward existing customers and entice new comers on other hand. Based on business model of Amit and Zott, i.e. the four constructs of eValue framework (efficiency, complementarities, lock-in, and novelty), four hypotheses have been formulated to test the differences in the drivers of e-Business value creation between Jordanian and foreign banks. A survey questionnaire in a form of paper-and-pencil was delivered personally to 200 employees from four main Jordanian banks and 200 employees from four foreign banks working in Jordan. The questionnaire was formed and constructed to test the proposed hypotheses. the findings in this study based on the SEM and T-test analyses, revealed important implications that will help banks’ managers to make well-informed decisions and policies regarding investments and resources allocation for implementing eBusiness strategies and ventures. The paper concludes with discussing the importance of these findings for practitioners and for future research on value accrued from e-Business services.
Translating Principles of Translation: Cross-Cultural and Multi-Brain Perspectives

Yan Fu’s 譯事三難 has rarely been directly challenged but is frequently compared with Tytler’s Principles of Translation. These two sets of principles match both in number and in the exact order of three parallel concepts. Given the canonical status of Tytler’s principles since its publication in 1790, it is hard to imagine that Yan was not influenced by Tytler in forming his principles. Following this assumption, we explore possible accounts of why and how Tytler’s three Principles of Translation could be “translated” into Yan’s “信達雅” “Xìn-Dá-Yǎ.” We note that Tytler’s was ranked in descending order of importance: the First General Law, which is the most important, requires a complete transcript of the original ideas (i.e., Xìn), whereas expressing the style and manner of the original writing (i.e., Dá) and achieving the ease of the original composition (i.e., Yǎ) are supplementary as the Second and Third General Laws. Yet Yan’s principles tend to be understood in reverse order of importance. We explore this mismatch from cross-cultural and multi-brain perspectives based on a comparable corpora approach. Through comparing BNC with the Gigaword Chinese Corpus, it is revealed that cultural differences in the meanings of the ordinals first and third lead to the overlook of the foundational concept in English that “the first principle” is the most important, and the mistaking of the third law (Yǎ) as the highest one in China. This reconfiguration of cultural meanings underlines the nature of translation as a multi-brain activity situated in cultural contexts.
Fisheye Camera Calibration with Indoor 3D Calibration Field

Cameras equipped with fisheye lenses have large distortion, and conventional camera calibration methods are no longer applicable. In this paper, we considered the radial distortion, tangential distortion, and thin lenses distortion of fisheye camera. we take the equidistant projection imaging model of fisheye camera as an example to derive the three-point common line constraint model under equidistant projection, namely spatial resection model of fisheye camera. Firstly, the fisheye camera spatial resection model is derived, the initial values of fisheye image radius and image center coordinates are calculated by curve fitting method, then the intrinsic parameters of fisheye lenses are calculated accurately by least square method, and finally spatial intersection is carried out to calculate the ground control point coordinates for verification. Our method takes a short time, is simple and satisfies the requirements of real-time calibration.
Explore Coarse-Grained Structures for Syntactically Controllable Paraphrase Generation

Syntactically controlled paraphrase generation can produce diverse paraphrases by exposing syntactic control, where both semantic preservation and syntactic variations are two important factors. Previous works mainly focus on using fine-grained syntactic structures (e.g., full parse tree) as syntactic control. While these methods can achieve excellent syntactic controllability, leads to failing to preserve the semantics of the input sentence. The main reason is that it is difficult to retrieve perfectly compatible syntactic structures with the input sentences. In this paper, we explore coarse-grained syntactic structures to trade-off semantic preservation and syntactic variations. Furthermore, to improve semantic preservation and syntactic controllability, we propose a Syntax Attention-Guided Paraphrase (SAGP) model that can correctly select syntactic information according to the current state for surface realization. Experiment results show that SAGP outperforms the previous state-of-the-art method under the same setting. Additionally, we validate that using coarse-grained structures can generate more semantically reasonable text without affecting the syntactic controllability.