Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Object Detection using Integrated Approach

Object detection in images, videos, and text detection using audio files becomes more complex. Classification of image objects from the static images I,e detecting the objects in static images, and detecting the object with a specific boundary box is called object localization. This can apply to videos also. Extracting the text from audio files is part of multimodal. Many issues are identified from extracting the required data from this entire multimodal. In this paper, the two-way object detection and localization with Ensemble Object Detection and Localization framework (EODLF) are introduced to overcome significant issues such as overlapping in object detection. This consists of two algorithms, one for object detection from static images using accurate bounding boxes for objects. To improve object detection, rapid simultaneous localization and mapping (RSLAM) are introduced to improve the accuracy. To detect objects in videos, images and recognize the text from the audios by using advanced short and long-range object linking which is called Advanced Tubelet Rendering integrated with Region Proposal Network (RPN) is used to detect objects with bounding boxes with high accuracy such as 98.78 and also this shows the count of the objects and names of the objects in the videos and images. Another advantage of this system is to detect the frequent words in audio also. The performance of the proposed approach is compared with various state-of-art approaches.
Visual Assistance in Development and Validation of Bayesian Networks for Clinical Decision Support

The development and validation of Clinical Decision Support Models (CDSM) based on Bayesian networks (BN) is commonly done in a collaborative work between medical researchers providing the domain expertise and computer scientists developing the decision support model. Although modern tools provide facilities for data-driven model generation, domain experts are required to validate the accuracy of the learned model and to provide expert knowledge for fine-tuning it while computer scientists are needed to integrate this knowledge in the learned model (hybrid modeling approach). This generally time-expensive procedure hampers CDSM generation and updating. To address this problem, we developed a novel interactive visual approach allowing medical researchers with less knowledge in CDSM to develop and validate BNs based on domain specific data mainly independently and thus, diminishing the need for an additional computer scientist. In this context, we abstracted and simplified the common workflow in BN development as well as adjusted the workflow to medical experts’ needs. We demonstrate our visual approach with data of endometrial cancer patients and evaluated it with six medical researchers who are domain experts in the gynecological field.
Quantum Dot Phase Transition Simulation with Hybrid Quantum Annealing via Metropolis-Adjusted Stochastic Gradient Langevin Dynamics

We report a hybrid quantum-classical simulation approach for simulating the optical phase transition observed experimentally in the ultrahigh-density type-II InAs quantum dot array. A hybrid simulation scheme, which contains stochastic gradient Langevin dynamics (a well-known Bayesian machine learning algorithm for big data) along with adiabatic quantum annealing, is developed to reproduce the experimentally observed phase transition. By implementing the simulation scheme into a quantum circuit, we successfully verified the phase transition observed in the experiment. Our work demonstrates for the first time the feasibility of hybridizing quantum computation with classical Langevin dynamics for the analysis of carrier dynamics and quantum phase transition of the quantum dot.
Recovering a Prior from a Posterior: Some Parameterizations of Jeffrey Conditioning

Given someone’s fully specified posterior probability distribution q and information about the revision method that they employed to produce q, what can you infer about their prior probabilistic commitments? This question provides an entrée into a thoroughgoing discussion of a class of parameterizations of Jeffrey conditioning in which the parameters furnish information above and beyond that incorporated in $$q$$ . Our analysis highlights the ubiquity of Bayes factors (ratios of new to old odds) in the study of probability revision.
Improving Mobile Device Security by Embodying and Co-adapting a Behavioral Biometric Interface

At present, interfaces between users and smart devices such as smart phones rely primarily on passwords. This has allowed for the intrusion and perturbation of the interface between the user and the device and has compromised security. Recently, Frank et al. have suggested that security could be improved by having an interface with biometric features of finger swiping. This approach has been termed touchalytics, in maintaining cybersecurity. The number of features of finger swiping have been large (32) and have been made available as a public database, which we utilize in our study. However, it has not been shown which of these features uniquely identify a particular user. In this paper, we study whether a subset of features that embody human cognitive motor features can be used to identify a particular user. We consider how the security might be made more efficient embodying Principal Component Analysis (PCA) into the interface, which has the potential of reducing the features utilized in the identification of intruders. We compare the accuracy and performance of the reduced feature space to that of having all the features. Embodying a robust continuous authentication system will give users an extra layer of security and an increased sense of peace of mind if their devices are lost or stolen. Consequently, such improvements may prevent access to sensitive information and thus will save businesses money. Consequently, such improvements may prevent access to sensitive information and thus will save businesses money. If continuous authentication models become successful and easily implementable, embodiment and co-adaptation of user authentication would inhibit the growing problem of mobile device theft.
Evaluation of Chinese Natural Language Processing System Based on Metamorphic Testing

A natural language processing system can realize effective communication between human and computer with natural language. Because its evaluation method relies on a large amount of labeled data and human judgment, the question of how to systematically evaluate its quality is still a challenging task. In this article, we use metamorphic testing technology to evaluate natural language processing systems from the user’s perspective to help users better understand the functionalities of these systems and then select the appropriate natural language processing system according to their specific needs. We have defined three metamorphic relation patterns. These metamorphic relation patterns respectively focus on some characteristics of different aspects of natural language processing. Moreover, on this basis, we defined seven metamorphic relations and chose three tasks (text similarity, text summarization, and text classification) to evaluate the quality of the system. Chinese is used as target language. We extended the defined abstract metamorphic relations to these tasks, and seven specific metamorphic relations were generated for each task. Then, we judged whether the metamorphic relations were satisfied for each task, and used them to evaluate the quality and robustness of the natural language processing system without reference output. We further applied the metamorphic test to three mainstream natural language processing systems (including BaiduCloud API, AliCloud API, and TencentCloud API), and on the PWAS-X datasets, LCSTS datasets, and THUCNews datasets. Experiments were carried out, revealing the advantages and disadvantages of each system. These results further show that the metamorphic test can effectively test the natural language processing system without annotated data.
Outline of an Intensional Theory of Truth

We expand on the fixed point semantic approach of Kripke via the addition of two unary intensional operators: a paradoxicality operator Π where Π(Φ) is true at a fixed point if and only if Φ is paradoxical (i.e., if and only if Φ receives the third, non-classical value on all fixed points that extend the current fixed point), and an unbounded truth operator Υ⊤ where Υ⊤(Φ) is true at a fixed point if and only if any fixed point extending the current fixed point can be extended to one on which Φ receives the value true. We prove a generalized version of Kripke's fixed point theorem guaranteeing the existence of models of this new language, as well as an expressive completeness result. We conclude with an exploration of the significant improvements in expressive power that result from the addition of these new operators, and we precisely identify what still cannot be said on this intensional extension of the Kripkean framework.
Efficient Perceptual Loss based on Low-rank Tucker Decomposition

The perceptual loss function has been used successfully in image transformation for capturing high-level features from images in pre-trained networks. Standard perceptual losses require numerous parameters to represent features in networks; thus, it is not proper to resource-constrained devices. Hence, we propose a compressing perceptual-loss-oriented low-rank Tucker decomposition optimized with High-Order Orthogonal Iteration. Additionally, to decide an optimal low-rank in decomposition, we used variational Bayesian matrix factorization. In the technique thereof, salient features are extracted more efficiently. To the best of our knowledge, we are the first to consider curtailing redundancies in feature maps via low-rank Tucker decomposition. Experimental results in style transfer tasks demonstrate that our method not only yields similar qualitative and quantitative results as that of the original version but also reduces memory requirement by approximately 34%.
CGC: a Scalable Python Package for Co- and Tri-Clustering of Geodata Cubes

Multidimensional data cubes are increasingly ubiquitous, in particular in the geosciences.Clustering techniques encompassing their full dimensionality are necessary to identify patterns "hidden" within these cubes.Clustering Geodata Cubes (CGC) is a Python package designed for partitional clustering, which identifies groups of similar data across two (e.g., spatial and temporal) or three (e.g., spatial, temporal, and thematic) dimensions.CGC provides efficient and scalable co-and tri-clustering functionality appropriate to analyze both small and large datasets as well as a cluster refinement functionality that supports users in their quest to make sense of complex datasets.
The Role of Digital Competencies in the Process of Digital Transformation

Digital transformation represents a connective tissue of the global economic system and therefore is an unavoidable element in the process of improvement, both developed and underdeveloped countries. In contrast to digitalization, which was the initiator of digital transformation and relied exclusively on the introduction of digital technologies, digital transformation is interpreted as much wider and requires a special set of skills and competencies. From the perspective of technological innovations, which is the key segment of this process, it is necessary to recognize relevant elements that ensure that the process of digital transformation runs smoothly. Initially, in order to identify the digital competencies of which the process consists, it is necessary to analyze the process of digital transformation itself. After the analysis, the focus shifts to the digital competencies needed to fulfill the identified skills. In this paper, an analysis of digital transformation is performed and the necessary competencies that employees need to have for this process to be successful within the business ecosystem are identified. Relying on DIGCOMP as guidance for digital competencies, recommendations and suggestions were formed in order to connect these two areas.
Unsupervised Learning Based Focal Stack Camera Depth Estimation

We propose an unsupervised deep learning based method to estimate depth from focal stack camera images. On the NYU-v2 dataset, our method achieves much better depth estimation accuracy compared to single-image based methods.
Continued Supporting a Systematic Literature Review by Applying Text Mining Methods

In the present paper, a framework for the continued supporting a systematic literature review (SLR) is proposed, which includes the application of text mining methods in order to automate the classification of scientific publications and the more in-depth analysis of their content. For this purpose, a dataset is created from the titles, abstracts and keywords of papers, included in a systematic literature review on the application of semantic technologies in bibliographic databases. Data analytics methods are applied - frequency analysis of words and word combinations; linear regression for trend exploration; text classification, where the categories are the applied semantic technologies or the researched problems in accordance with a pre-defined classification framework. The vector space model enriched with PMI (pointwise mutual information) measure is used for the classification. An assessment of the text classification performance in terms of various measures is made and the obtained results are summarized.
Bayesian-Inference Embedded Spline-Kerneled Chirplet Transform for Spectrum-Aware Motion Magnification

The ability to discern subtle image changes over time is useful in applications such as product quality control, civil engineering structure evaluation, medical video analysis, music entertainment, and so on. However, tiny yet useful variations are often combined with large motions, which severely distorts current video amplification methods bounded by external constraints. This paper presents a novel use of spectra to make motion magnification robust to large movements. By exploiting spectra, artificial limitations and the magnification of small motions are avoided at similar frequency levels while ignoring large ones at distinct spectral pixels. To achieve this, this paper constructs spline-kerneled chirplet transform (SCT) into an empirical Bayesian paradigm that applies to the entire time series, giving powerful spectral resolution and robust performance to noise in nonstationary nonlinear signal analysis. The important advance reported is Bayesian-rule embedded SCT (BE-SCT); two numerical experiments show its superiority over current approaches. For applying to spectrum-aware motion magnification, an elaborate analytical framework is established that captures global motion, and use of the proposed BE-SCT for dynamic filtering enables a frequency-based motion isolation. Our approach is demonstrated on real-world and synthetic videos. This approach shows superior qualitative and quantitative results with less visual artifacts and more local details over the state-of-the-art methods.
A Method Design of English Teaching System Based on Video Feedback Method

The traditional English teaching mode is that the teacher simply imparts textbook knowledge and students understand and absorb it. However, this method has obvious problems; that is, it is difficult to ensure that students can quickly understand the content taught and cannot get fast feedback. The video feedback method is a method of English teaching combined with audiovisual technology. Teachers can use video technology to record key English knowledge through video and then give feedback and explain the learning through video. At the same time, the intelligent video feedback system teaching method will greatly improve the teaching quality of English classroom and make students’ learning more fun. This paper mainly designs the English teaching system based on the video feedback method and finally realizes the intelligent feedback scheme of the English teaching system. Firstly, the neural network method, classification method, and video shooting technology are used to extract and predict the characteristics of students’ classroom expressions, speech, and so on, and analyze through the video feedback system of English classroom. The research results show that the classification method proposed in this study can better complete the body movements such as student expressions and speech collected by the English teaching system, and the neural network method can more accurately predict and feed back the teaching content through the video feedback method with the largest error. It was only 2.98%, and the linear correlation also reached more than 0.98. The minimum error of the video feedback information is only 0.95%, and the prediction errors of the other two kinds of English classroom information are also 2%. The classification and prediction of intelligent video feedback information have achieved good results.
Bayesian Modeling of Sequential Discoveries

We aim at modeling the appearance of distinct tags in a sequence of labeled objects. Common examples of this type of data include words in a corpus or distinct species in a sample. These sequential discoveries are often summarized via accumulation curves, which count the number of distinct entities observed in an increasingly large set of objects. We propose a novel Bayesian method for species sampling modeling by directly specifying the probability of a new discovery, therefore, allowing for flexible specifications. The asymptotic behavior and finite sample properties of such an approach are extensively studied. Interestingly, our enlarged class of sequential processes includes highly tractable special cases. We present a subclass of models characterized by appealing theoretical and computational properties, including one that shares the same discovery probability with the Dirichlet process. Moreover, due to strong connections with logistic regression models, the latter subclass can naturally account for covariates. We finally test our proposal on both synthetic and real data, with special emphasis on a large fungal biodiversity study in Finland. Supplementary materials for this article are available online.
Runtime analysis of convex evolutionary search algorithm with standard crossover

Evolutionary Algorithms (EAs) with no mutation can be generalized across representations as Convex Evolutionary Search algorithms (CSs). However, the crossover operator used by CSs does not faithfully generalize the standard two-parents crossover: it samples a convex hull instead of a segment. Segmentwise Evolutionary Search algorithms (SESs) are defined as a more faithful generalization, equipped with a crossover operator that samples the metric segment of two parents. In metric spaces where the union of all possible segments of a given set is always a convex set, a SES is a particular CS. Consequently, the representation-free analysis of the CS on quasi-concave landscapes can be extended to the SES in these particular metric spaces. When instantiated to binary strings of the Hamming space (resp. d-ary strings of the Manhattan space), a polynomial expected runtime upper bound is obtained for quasi-concave landscapes with at most polynomially many level sets for well-chosen population sizes. In particular, the SES solves Leading Ones in at most 288nln[4n(2n+1)] expected fitness evaluations when the population size is equal to 144ln[4n(2n+1)].
Is Felix Salten the author of the Mutzenbacher novel (1906)? Yes and no

Josefine Mutzenbacher oder die Geschichte einer Wienerischen Dirne von ihr selbst erzählt, published in Vienna in 1906, represents one of the most fascinating cases of attribution of authorship in German literature. Although Josefine Mutzenbacher is usually attributed to Felix Salten, the author of the world-famous Bambi (1923), the novel’s authorship has never been confirmed, and many other candidates have been named as potential authors. Among them is Arthur Schnitzler, who published Reigen, a cycle of amorous adventures in Viennese society, in 1903. Some scholars, instead, have attributed the novel to such lesser-known writers as Ernst Klein and Willi Handl. The controversy surrounding the authorship of Josefine Mutzenbacher was the starting point for our stylometric analyses, and our results help to answer some unresolved questions in a debate that has lasted for more than 100 years. The analyses were performed using the R package Stylo, which enables an efficient application of Burrows’ Delta and its variants. Focusing on both the entire text and on the final pages, two different types of analysis were carried out: one combines 1200 different stylometric methods to compare the candidate authors Salten, Schnitzler, Bahr, Altenberg, Hofmannsthal, Klein and Handl; the other verifies the attribution using the ‘impostors’ method. The results show that the most probable author is Felix Salten, while none of the candidates could be identified as the author of the final pages, confirming the hypothesis that the text was left unfinished by Salten and completed by an as-yet-unidentified ghost-writer.
Unsupervised Domain Adaptation for Environmental Recognition in Crane Operations

In loading work with a helicopter or a crane, the body itself recognizes an environment, and in order to develop a system that judges whether the commands and operations by human are safe, it needs to realize semantic recognition of the environment, distance recognition to an obstacle, and classification and pursuit of moving objects with high precision using sensors. A method of realizing semantic segmentation, depth estimation and optical flow simultaneously from a camera image had been proposed with a multitasking DNN that took account of the posture and speed of a drone considering a loading work in the air, and it was proved to be useful in a simulated environment. Note however that the model learned in the simulation environment is not a thing suitable for the environmental recognition in a real world. Therefore, this paper aims to develop an environmental recognition system that can be used in the real world by conducting a domain adaptation with adversarial learning. The usefulness of the domain adaptation technique in the proposed multitasking DNN is verified by carrying out environmental recognition of the actual image acquired from the boom tip of a crane.
Rapid, Secure and Vedic Approach to Cryptography

An efficient approach to improve security is given by double point multiplication. The performance is further improved by replacing the Conventional Array multiplier with Vedic multiplier and replacing scalar multiplication with left shift operation. The proposed multiplier periodically processes data as 64-bit double words repeatedly. This multiplier when employed in the ECC algorithm drastically reduces the total area utilization. The shifting operation increases the security at every instant. The simulation of the proposed design is done using Xilinx 14.7 software. Experimental findings show a remarkable improvement in the speed of the process and its security.