Reviewerly/exHarmony huggingface dataset:
Pelatihan Penggunaan Sistem Inventory Data Barang Pada Gudang CV.Tanaya

CV. Tanaya is one of the companies engaged in the field of suppliers, this CV buys raw materials in several Agents. The availability of goods is very influential in production planning, therefore inventory data is needed. Goods inventory data processing in the CV. Tanaya is still manual, namely for recording data using a stock card. This raises the motivation to design and conduct training in the use of information systems about inventory data items, especially in the warehouse CV. Tanaya The method used consists of two stages, the first is observation and interviews with the CV. The second stage is to carry out a community education approach, in this case training the use of the system. This aims to make it easier for companies to manage data so that it is faster, accuracy in data processing and improve accuracy in managing the entry and exit of goods stock in the warehouse of CV. Tanaya.
Automatic Database Segmentation using Hybrid Spectrum -Visual Approach

Nowadays automated segmentation of speech signals has been attracted many of researchers all-over the world, Many speech processing systems require segmentation of speech waveform into principal acoustic units. In this research, TIMIT DataBase (DB) is utilized to carry on this process and justify its operation or results. Thus, this paper presents a novel method of segmentation of speech phonemes, where the proposed strategy helps in the selection of appropriate feature extraction technique for speech segmentation. There are three main techniques of feature extraction used in our research; the first technique is the Mel Frequency Cepstral Coefficient (MFCC), the second technique is known by Best Tree Encoding (BTE), while the third is Image Normalized Encoder (INE), which is a hybrid technique between the Best Tree Image (BTI), and the Convolution Neural Network (CNN) ResNet-50. Then, data are trained using a hybrid model that consists of Hidden Markov Model (HMM), and Gaussian Mixture Model (GMM) to improve the performance of automatic speech recognition. The proposed model is tested and verified against the most widely used feature Mel Frequency Cepstral Coefficient (MFCC) plus delta and delta-delta coefficients (39 parameters) to evaluate its performance. This approach has the potential to be used in applications such as automatic speech recognition and automatic language identification. The experimental results show that BTE technique achieved the highest success rate (𝜂) (92.64%) than using the (INE) technique. However, the INE technique gives confusion success rate for Tr and NTr of values 97.1% and 99.1%, respectively.
Physics informed deep learning to super-resolve and cross-calibrate solar magnetograms

Abstract Super-resolution techniques aim to increase the resolution of images by adding detail. Compared to upsampling techniques reliant on interpolation, deep learning-based approaches learn features and their relationships across the training data set to leverage prior knowledge on what low resolution patterns look like in higher resolution images. As an added benefit, deep neural networks can learn the systematic properties of the target images (i.e.\ texture), combining super-resolution with instrument cross-calibration. While the successful use of super-resolution algorithms for natural images is rooted in creating perceptually convincing results, super-resolution applied to scientific data requires careful quantitative evaluation of performances. In this work, we demonstrate that deep learning can increase the resolution and calibrate space- and ground-based imagers belonging to different instrumental generations. In addition, we establish a set of measurements to benchmark the performance of scientific applications of deep learning-based super-resolution and calibration. We super-resolve and calibrate solar magnetic field images taken by the Michelson Doppler Imager (MDI; resolution ~2"/pixel; science-grade, space-based) and the Global Oscillation Network Group (GONG; resolution ~2.5"/pixel; space weather operations, ground-based) to the pixel resolution of images taken by the Helioseismic and Magnetic Imager (HMI; resolution ~0.5"/pixel; last generation, science-grade, space-based).
‘그러니까’의 의미 기능과 실현 양상 연구

The purpose of this study is to classify the pragmatic-semantic function of ‘Geureonikka’ and to explore the pragmatic-semantic function and realization aspect of ‘Geureonikka’ through an analysis of a written Korean corpus. Therefore, Sweetser’s(1990) cognitive approach to the analysis of pragmatic ambiguity was examined, and his theory was applied to describe the realization aspect in a written Korean corpus of the conjunctive adverb ‘Geureonikka’. Subsequently, four findings were identified. First, the semantic function of ‘-nikka’ correlates with that of ‘Geureonikka’. and the connective ending ‘-nikka’ was verified as a cause for the semantic function formation of ‘Geureonikka’. Second, similar to ‘because’ in English, ‘Geureonikka’ was used in every domain: content connection, epistemic connection, and speech-act connection. Third, ‘Geureonikka’ in a written Korean corpus was extensively used in epistemic connection while it was infrequently used in content connection. Fourth, the syntactic position of ‘Geureonikka’ was determined by the semantic function of ‘Geureonikka’. The limitations of this study were also identified: (i) The analyzed genre of a written Korean corpus was limited, and (ii) further research is needed to investigate similar conjunctive adverbs to ‘Geureo’ to reveal their differences from ‘Geureonikka’. Nevertheless, this research is meaningful as the pragmatic-semantic function of ‘Geureonikka’ was classified and the realization aspect of ‘Geureonikka’ in a written Korean corpus was demonstrated. (Yeungnam University)
Trimming the Hedges: An Algebra to Tame Concurrency

chapter Share on Trimming the Hedges: An Algebra to Tame Concurrency Author: Georg Struth Search about this author Authors Info & Claims Theories of Programming: The Life and Works of Tony HoareOctober 2021 Pages 317–346https://doi.org/10.1145/3477355.3477370Online:02 October 2021Publication History 0citation1DownloadsMetricsTotal Citations0Total Downloads1Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Invisibility Cloak using Color Detection and Segmentation with Open CV

Have you ever thought of making visible things invisible, just like the Harry Potter? Have you ever thought how does one supersede backgrounds and add effects in a movie? The cloak was magical and invisible in Harry Potter, the movie. As we know there is no magic and no invisible cloak which exists in the world. It’s all about the graphics tricks. The concept of an invisibility cloak is a mixture of science, fantasy, and the collective imagination. This paper helps to create one’s own ‘Invisibility Cloak’.It will make use of Python and OpenCV module specifically targeting Image Processing and Image Segmentation to create a false sense of invisibility in the frame. It will explore how an object of a specific color or texture can be manipulated using the OpenCV library of python. To achieve this, initially we’ll be capturing and storing the backdrop frame . Thereafter we’ll be identifying the red coloured fabric by making use of the above mentioned algorithms. Then we’ll segment out the red colored fabric by generating a mask and then finally , we’ll generate the final augmented(magical) output to create Invisibility cloak. These steps are discussed deeper in the paper.
Modelling Neighbor Relation in Joint Space-Time Graph for Video Correspondence Learning

This paper presents a self-supervised method for learning reliable visual correspondence from unlabeled videos. We formulate the correspondence as finding paths in a joint space-time graph, where nodes are grid patches sampled from frames, and are linked by two types of edges: (i) neighbor relations that determine the aggregation strength from intra-frame neighbors in space, and (ii) similarity relations that indicate the transition probability of inter-frame paths across time. Leveraging the cycle-consistency in videos, our contrastive learning objective discriminates dynamic objects from both their neighboring views and temporal views. Compared with prior works, our approach actively explores the neighbor relations of central instances to learn a latent association between center-neighbor pairs (e.g., "hand -- arm") across time, thus improving the instance discrimination. Without fine-tuning, our learned representation outperforms the state-of-the-art self-supervised methods on a variety of visual tasks including video object propagation, part propagation, and pose keypoint tracking. Our self-supervised method also surpasses some fully supervised algorithms designed for the specific tasks.
프로모션 사회의 어린이 유튜브 담화 연구 : 부모와 자녀가 출연하는 어린이 채널을 중심으로

The purpose of this study is to examine children's YouTube contents that intensively show the characteristics of a promotional society from the perspective of critical discourse studies. This starts to view of the concept of discourse as ‘use of language in a social context’. Discourse can be approached in terms of texts, discourse practices, and sociocultural practices. And the analysis was carried out within the dialectical relationship of these three dimensions. Children's YouTube discourse superficially pursues an entertainment genre, but has the characteristics of an advertising genre. The discourse practice of YouTube text makes it natural to watch a commercial presented before watching main contents. In addition, it commercializes play activities between parents and children in the home domain. Representing ‘play’ and ‘home’ at the text level as excessively abundant goods is related to ‘discourse on happiness through material’. In terms of discourse practices, the value of the play activities of parents and children as media products, and the guarantee of (advertising) viewing act for consumption of these texts, is based on the characteristics of the promotion and consumption society. Capturing the ideology of fetishism that penetrates these three dimensions contributes to the recognition of the act of watching as labor.
Learning Multi-Layered GBDT Via Back Propagation

Deep neural networks are able to learn multi-layered representation via back propagation (BP). Although the gradient boosting decision tree (GBDT) is effective for modeling tabular data, it is non-differentiable with respect to its input, thus suffering from learning multi-layered representation. In this paper, we propose a framework of learning multi-layered GBDT via BP. We approximate the gradient of GBDT based on linear regression. Specifically, we use linear regression to replace the constant value at each leaf ignoring the contribution of individual samples to the tree structure. In this way, we estimate the gradient for intermediate representations, which facilitates BP for multi-layered GBDT. Experiments show the effectiveness of the proposed method in terms of performance and representation ability. To the best of our knowledge, this is the first work of optimizing multi-layered GBDT via BP. This work provides a new possibility of exploring deep tree based learning and combining GBDT with neural networks.
Improving Adversarial Robustness for Free with Snapshot Ensemble

Adversarial training, as one of the few certified defenses against adversarial attacks, can be quite complicated and time-consuming, while the results might not be robust enough. To address the issue of lack of robustness, ensemble methods were proposed, aiming to get the final output by weighting the selected results from repeatedly trained processes. It is proved to be very useful in achieving robust and accurate results, but the computational and memory costs are even higher. Snapshot ensemble, a new ensemble method that combines several local minima in a single training process to make the final prediction, was proposed recently, which reduces the time spent on training multiple networks and the memory to store the results. Based on the snapshot ensemble, we present a new method that is easier to implement: unlike original snapshot ensemble that seeks for local minima, our snapshot ensemble focuses on the last few iterations of a training and stores the sets of parameters from them. Our algorithm is much simpler but the results are no less accurate than the original ones: based on different hyperparameters and datasets, our snapshot ensemble has shown a 5% to 30% increase in accuracy when compared to the traditional adversarial training.
On Margin Maximization in Linear and ReLU Networks

The implicit bias of neural networks has been extensively studied in recent years. Lyu and Li [2019] showed that in homogeneous networks trained with the exponential or the logistic loss, gradient flow converges to a KKT point of the max margin problem in the parameter space. However, that leaves open the question of whether this point will generally be an actual optimum of the max margin problem. In this paper, we study this question in detail, for several neural network architectures involving linear and ReLU activations. Perhaps surprisingly, we show that in many cases, the KKT point is not even a local optimum of the max margin problem. On the flip side, we identify multiple settings where a local or global optimum can be guaranteed.
Struktur Cerita pada Iklan

Although advertisements have a short duration, just like cinema films, commercial advertisements cannot be separated from form or story elements. Some advertisements even present it like a movie in the cinema, with a strict causal pattern. The problem is, despite having a story that includes several events, placing those events is the key to the audience's understanding of the advertisement itself. The sequence of events that can not be understood by the audience, of course, will have a bad impact on advertising. The story is not easily understood by the audience. Therefore, the structure in arranging a series of events per event in advertising is very important. Which event was first and which event was later, is an arrangement like composing a particular building.
Optimizing RSA cryptosystem using Hermite polynomials

The Security is requested to relocate paramount information across the networks. To protect the confidential data from hacking, this paper describes a process to increase the security of the RSA algorithm by creating additional layer protection for it using Hermite polynomials which will be represented as a square matrix, its calculation is not complicated. In the RSA process, we need to choose very large numbers that lead to complex operations which require a long computation time, while in proposed encryption due to Hermite key we don't need that because two layers of encryption give robust safeness to the ciphertext from dangers of hackers due to the hard of breakable.
Research on facial expression recognition based on Multimodal data fusion and neural network

Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadvantages of low accuracy and low robustness. In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed. The algorithm is based on the multimodal data, and it takes the facial image, the histogram of oriented gradient of the image and the facial landmarks as the input, and establishes CNN, LNN and HNN three sub neural networks to extract data features, using multimodal data feature fusion mechanism to improve the accuracy of facial expression recognition. Experimental results show that, benefiting by the complementarity of multimodal data, the algorithm has a great improvement in accuracy, robustness and detection speed compared with the traditional facial expression recognition algorithm. Especially in the case of partial occlusion, illumination and head posture transformation, the algorithm also shows a high confidence.
THEATRICAL POST-CORPOREITY. DIGITIZING THE BODY IN A NEW CULTURE OF SOCIAL INTERACTIONS

The article deals with such a cultural phenomenon as the post-corporeity, that is, what has replaced the traditional practice of bodily interactions and sensations, as a result of the temporary total creative drift of culture into cyberspace. Virtual theater experiments represent a complete subtraction, the exclusion of the actor’s and audience’s body from the dramatic text, the demarcation of the sensory and tactile base of the performance itself. The post-drama emphasis on visualization has been replaced by a new round of verbalization, where the story is forced to shorten in favor of the brevity of the virtual genre due to technological difficulties. Digital theater offers to discover the erogenous zones of the virtual body in order to freely respond to the cyber-desires of the viewer.
An axiom system of probabilistic Mu-calculus

Mu-calculus (a.k.a. μTL) is built up from modal/dynamic logic via adding the least fixpoint operator μ. This type of logic has attracted increasing attention since Kozen's seminal work. PμTL is a succinct probabilistic extension of the standard μTL obtained by making the modal operators probabilistic. Properties of this logic, such as expressiveness and satisfiability decision, have been studied elsewhere. We consider another important problem: the axiomatization of that logic. By extending the approaches of Kozen and Walukiewicz, we present an axiom system for PμTL. In addition, we show that the axiom system is complete for aconjunctive formulas.
The Role of VR in Influencing Tourism Consumers’ Attitudes Towards a Tourist Destination: An Abstract

Virtual reality (VR) has been outlined as one of the most important technological developments to influence the tourism industry due to its ability to engage consumers and to market tourism destinations. The use of VR has spread across the entire tourism industry with the presence of VR in hotel previews, destination branding, tourism experiences including museums, theme parks, and cultural heritage sites (see: Bogicevic et al. 2019; Griffin et al. 2017; Wei et al. 2019). The application of VR in each of these sectors is based on the premise that the technology can transform experiences and positively influence behaviour (Zeng et al. 2020). Despite the growing interest in VR by both tourism consumers and tourism marketers, both largely rely on the presentation of destinations through a computer-mediated website displaying basic non-dynamic images of the destination or venue (Israel et al. 2019). While basic non-dynamic images can present vivid non-verbal information to tourism consumers, VR has the capability to provide numerous verbal and non-verbal sensory information including visual, haptic, gustatory, auditory or olfactory cues (Miller and Stoica 2004). The depth of immersion and presence experienced within VR distinguish it from any other technology (Wei et al. 2019). Thus, VR provides a fundamentally different experience to consuming destination information in comparison to basic non-verbal images due to the sensory and media rich content (Wei et al. 2019). The purpose of this research is to understand the role of VR in influencing tourism consumers’ attitudes towards a tourist destination. Through a lab-based experiment with 204 tourism consumers this research found that following a VR preview experience of a tourist destination consumers will have more positive attitudes towards the tourist destination than prior to the VR experience. Interestingly, in comparison, a website preview has no significant effect on influencing tourism consumers’ previously held attitudes towards the destination. More so, the results indicate that tourism consumers have more positive attitudes towards a tourist destination in a VR preview in comparison to a less immersive website preview. Thus, the inherent interactive, immersive, and sensory rich attributes of VR have a positive effect on tourism consumers’ attitudes towards a destination. Hence, for managers, the use of pop-up VR stations in shopping malls or other entertainment venues would be an advantageous marketing strategy, while also providing VR previews to download from relevant stores such as the Oculus store for consumers to use on their own devices within the comfort of their own home.
Ethics in criminological research of online communities

The aim of this paper is to outline and critically analyse the ethical dilemmas faced by criminologists tasked with online community research. Online communities and online content serve as a valuable sources of criminological knowledge about online crime and deviance as well as formal and informal norm-making and means of social control. From discussion forum texts and blogs to multimedia posts in open and closed social networking groups, from visual and video materials on Instagram, Youtube, or Tiktok to organized crime group data exchanges in publicly inaccessible communication channels, there is great diversity and variety of the contents and forms of online communication enacted by online communities. Correspondingly, research projects are different – some focusing on the content as a linguistic object, others focusing on social relations, social network structure, and its ethnographic characteristics, while many fall in between. In addition, depending on the research goals and sensitivity of the research questions, researchers may opt for active interaction or passive (and sometimes covert) observation. Therefore there is no one-size-fits-all ethical solution for approaching online communities in criminology. Based on an in-depth analysis of methodological literature, the paper suggests that online community research is largely a matter of situational ethics, wherein researchers must make situation-aware ethical decisions about several key issues. In particular, they should aim to choose and provide arguments regarding: 1) expectations of publicity or privacy in publicly accessible information; 2) the need for informed consent or absence of such need; 3) ensuring balance between anonymity and authorship attribution; 4) securing collected data; and 5) correctly assessing risks to the researched individuals and communities, and the researchers themselves.
Educational software and its impact on the learning process in university.

This chapter describes a study carried out in 2019 which evaluated the use of ICTs, specifically the LogicaUCAB educational software, and its effects on students’ learning of the Propositional Logic content from the Computing Logic course of the Computer Engineering degree of the Universidad Catolica Andres Bello (UCAB). Two groups of stu dents were selected: an experimental group, who used the LogicaU CAB tool in a laboratory during five weeks, one session per week, and a control group, which did not have access to the software. The official UCAB platform (Modulo7-Canvas) was also used for virtual activi ties. The research methodology was quasi-experimental, since groups were formed non-randomly. Finally, a written test was applied to both groups to verify results. These showed a positive effect on the academic performance of the students regarding the Propositional Logic content when using LogicaUCAB in the Computational Logic course. Based on this experience, a program that can be further applied and that contributes to student learning of Propositional Logic is formally detailed, through the use of an Educational Software and some other ICT tools.