Reviewerly/exHarmony huggingface dataset:
Levels of context: how textual analysis becomes discourse analysis: the case of Internet lifestyle media

Today the so-called problem of discourse should boil down to (1) identifying relevant con­textual variables, (2) matching these to specific linguistic categories, and (3) operationalizing the former vis-à-vis the latter. Having posited this, the purpose of the article is twofold. In a more theoretical sense, the purpose is to outline one possible model of context each 'tier' of which is potentially related to certain linguistic categories and linguistic analytical toolkits. The suggested model has five tiers of contextual variables and two dimensions cutting through the five tiers. These dimensions are discourse (in the most general sense — as one of the 'moments' of social practice) and individuals (theorized here in two ways — in terms of cognition and as a complex of semiotic resources used to 'perform' identities). In a more prac­tical sense, the purpose of the article is to use (certain fragments of) the model to analyze dis­course. The discourse used for such analysis is a lifestyle instruction video from YouTube. Methodologically, the analysis draws on (1) the category of engagement as described in the Appraisal Model within Systemic Functional Linguistics and (2) the interpretation of dis­course/text as simultaneously invoking different discourses and genres, as suggested in the Faircloughian approach to discourse analysis. When seen as a complex semiotic happening, the discourse is analyzed in terms of it being part of the YouTube media platform. It is con­tended that as a result of the communicative technology in question, which has drastically changed participatory frameworks for mediated communicative events of this sort, a minimal discursive unit of analysis and interpretation should include, in addition to the text and ac­companying modalities (the discourse of the original video), comments made by other users (collaborative discourse). When seen more through the Foucauldian lens, the discourse of the video is analyzed as a discursive and generic hybrid invoking several social practices associat­ed with consumer culture, which can (to a certain extent) be shown to have (pre)defined the contents, the language and possible interpretations by the tentative addressee.
ADSAttack: An Adversarial Attack Algorithm via Searching Adversarial Distribution in Latent Space

Deep neural networks are susceptible to interference from deliberately crafted noise, which can lead to incorrect classification results. Existing approaches make less use of latent space information and conduct pixel-domain modification in the input space instead, which increases the computational cost and decreases the transferability. In this work, we propose an effective adversarial distribution searching-driven attack (ADSAttack) algorithm to generate adversarial examples against deep neural networks. ADSAttack introduces an affiliated network to search for potential distributions in image latent space for synthesizing adversarial examples. ADSAttack uses an edge-detection algorithm to locate low-level feature mapping in input space to sketch the minimum effective disturbed area. Experimental results demonstrate that ADSAttack achieves higher transferability, better imperceptible visualization, and faster generation speed compared to traditional algorithms. To generate 1000 adversarial examples, ADSAttack takes 11.08s and, on average, achieves a success rate of 98.01%.
A comparative study of some models regarding the creation of the early universe

"The creation of Universe is a problem which receives a more and more complex answers. The theory of BIG BANG (more correct BIG FLASH because the sound does not propagate through vacuum), is a standard model leaving several open questions. One of them is: where the energy of Universe comes from? If it comes from somewhere then the BIG BANG is not a primordial beginning. A second problem is the further evolution: the standard model considers a “soup” of light where various particles [8] are formed by hazard at instants more or less justified. In authors’ opinion, an order shoul dexist from the very beginning. As regards the source of energy, the authors propose a possibility of energy appearance from “nothing” (creation ex nihilo of Theology) in agreement with the uncertainty principle of Heisenberg. Then the radiant energy is maintained for a longer time interval while a structuring by division of the Universe sphere takes place. This structuring is ended when neutrons (substance) appear by an effect of resonance. Then other particles are formed first by the neutron transformation. "
ZSE-VITS: A Zero-Shot Expressive Voice Cloning Method Based on VITS

Voice cloning aims to synthesize the voice with a new speaker’s timbre from a small amount of the new speaker’s speech. Current voice cloning methods, which focus on modeling speaker timbre, can synthesize speech with similar speaker timbres. However, the prosody of these methods is flat, lacking expressiveness and the ability to control the expressiveness of cloned speech. To solve this problem, we propose a novel method ZSE-VITS (zero-shot expressive VITS) based on the end-to-end speech synthesis model VITS. Specifically, we use VITS as the backbone network and add the speaker recognition model TitaNet as the speaker encoder to realize zero-shot voice cloning. We use explicit prosody information to avoid effects from the speaker information and adjust speech prosody using the prosody information prediction and prosody fusion methods directly. We widen the pitch distribution of the train datasets using pitch augmentation to improve the generalization ability of the prosody model, and we fine-tune the prosody predictor alone in the emotion corpus to learn prosody prediction of various styles. The objective and subjective evaluations of the open datasets show that our method can generate more expressive speech and adjust prosody information artificially without affecting the similarity of speaker timbre.
Unequal literacy development and access to online education in public versus private Panamanian schools during COVID-19 pandemic

National and international tests have yielded low reading comprehension results for education in Panama, although there is limited information regarding literacy development and performance. There are wide gaps in social inequality, access to technology, and public versus private school achievement. Considering this, after a year off from regular face-to-face classes and a partial transition to online education due to the COVID-19 pandemic, the present study utilizes existing data to carry out a pre-post comparison of the reading performance of fourth ( n = 167) and sixth ( n = 164) grade students in the province of Panama employing a subsample stratified by educational system for comparability (Mann–Whitney U test, α = 0.05). The pre-post comparison was also carried out independently in both the public ( n = 235) and private ( n = 106) systems, as well as an additional comparison of the average weekly hours of online academic engagement in both systems during the pandemic in fourth ( n = 117) and sixth grade ( n = 109). The results support a significant decrease in reading performance. Based on the comparative analysis, findings indicate that public school students interacted online with their teachers significantly less than their private schools’ counterparts; and that, in the same sample, only the public-school students exhibited a significant decrease in reading speed by phonological and lexical route with a medium effect size compared to pre-pandemic standards, greater than those reported in other contexts. This highlights the need to develop effective strategies to narrow the existing educational gaps in the country, which seem to have widened due to the pandemic, with particular emphasis on reading performance in primary school.
Low-cost integrated INS/GNSS using adaptive H∞ Cubature Kalman Filter

Abstract We proposed an adaptive H-infinity Cubature Kalman Filter (AH∞CKF) to improve the navigation accuracy of a highly manoeuvrable unmanned aerial vehicle (UAV). AH∞CKF fuses the Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) measurements. Traditional state estimation filters like extended Kalman filters (EKF) and cubature Kalman filters (CKF) assume Gaussian noises. However, their performance degrades for non-Gaussian noises and system uncertainties encountered in real-world applications. Thus, designing filters robust to noise and distribution is crucial. AH∞CKF combines H∞CKF design with an added adaptive factor to adjust the state estimation covariance matrix according to measurements by exploiting the square root method to yield more numerically stable results (SrAH∞CKF). We conducted multiple dynamically rich flight tests to validate our claims using a UAV equipped with a commercially well-known GNSS solution. Results show that the SrAH∞CKF state estimation outperforms EKF and CKF methods on average by 90% in various statistical measures.
Sarcasm Detection Base on Adaptive Incongruity Extraction Network and Incongruity Cross-Attention

Sarcasm is a linguistic phenomenon indicating a difference between literal meanings and implied intentions. It is commonly used on blogs, e-commerce platforms, and social media. Numerous NLP tasks, such as opinion mining and sentiment analysis systems, are hampered by its linguistic nature in detection. Traditional techniques concentrated mostly on textual incongruity. Recent research demonstrated that the addition of commonsense knowledge into sarcasm detection is an effective new method. However, existing techniques cannot effectively capture sentence “incongruity” information or take good advantage of external knowledge, resulting in imperfect detection performance. In this work, new modules are proposed for maximizing the utilization of the text, the commonsense knowledge, and their interplay. At first, we propose an adaptive incongruity extraction module to compute the distance between each word in the text and commonsense knowledge. Two adaptive incongruity extraction modules are applied to text and commonsense knowledge, respectively, which can obtain two adaptive incongruity attention matrixes. Therefore, each of the words in the sequence receives a new representation with enhanced incongruity semantics. Secondly, we propose the incongruity cross-attention module to extract the incongruity between the text and the corresponding commonsense knowledge, thereby allowing us to pick useful commonsense knowledge in sarcasm detection. In addition, we propose an improved gate module as a feature fusion module of text and commonsense knowledge, which determines how much information should be considered. Experimental results on publicly available datasets demonstrate the superiority of our method in achieving state-of-the-art performance on three datasets as well as enjoying improved interpretability.
General Software Platform and Content Description Format for Assembly and Maintenance Task Based on Augmented Reality

Augmented reality (AR) support systems have proven to be effective in supporting various tasks, but are not yet widely used in real-world applications due to their high development costs. Although several AR authoring tools have been proposed for content development to solve this problem, most AR systems developed with these tools are not compatible with industrial environments and task types, and have inflexible visualization styles. This study provides a systematic solution of combining the AR task support software platform and the general contents description format by expanding the degree of freedom of the visualization methods and improving the design process. The proposed solution adapts to different activities and environments, and optimizes content implementation and user experience. The user study was conducted to evaluate the task performance including processing time and errors and the user experience including the perceived cognitive load and usability of our solution by comparing with a conventional system. The results show that the proposed software platform could improve the compatibility with industrial environments and tasks, and reduce the workload and cognitive effort, although the task performance was the same as the conventional AR system.
Multi-level contour combination features for shape recognition

We present a novel multi-level contour combination feature for shape recognition. This combination feature effectively solves large intra-class changes and nonlinear deformations of object shapes, thereby enhancing the performance of shape recognition. First, we divide the shape contour into two levels: the sampling points and the contour fragments, where sampling points are used to describe the detailed information of a shape and contour fragments are used to represent the global feature of a shape. Second, we employ the Fisher vector (FV) approach to encode the local sampling point feature and contour fragment feature as high-level characteristics. Finally, we combine the high-level characteristics after FV encoding and perform shape recognition through a linear support vector machine (SVM) model. The proposed method has been assessed on three benchmark shape datasets, including the Animal, MPEG-7,and ETH-80 datasets. Our method achieves 92.70%, 99.26% and 98.32% classification accuracy on the Animal, MPEG-7, and ETH-80 datasets, respectively. In addition, our method can also be applied to the classification of objects in real-word scenes. We combine the Weizmann Horse and the ETHZ Cow real-world scene datasets, and our method achieves 99.25% classification accuracy on the combined dataset. The recognition results of our approach are better than prior state-of-the-art shape recognition methods, which demonstrate the effectiveness and superiority of our approach.
Methodological Materials on the Conduct of Forensic Examination: Basic Concepts, Similarities and Differences

The article discusses methodological materials on the conduct of forensic examination: methodological recommendations, standard expert techniques, forensic methodologies, standard operating procedures (SOP). As their characteristics are presented: basic concepts, structure, form of presentation, the scope of tasks to be solved, approval procedure. During the research the author has applied a universal dialectical method, a system of general scientific methods: observation, description, comparison, analysis and synthesis, classification. Also, a comparison of methodological materials was carried out to identify the completeness of their description, especially concerning the quality control of the obtained results. The author proposed a hypothesis that the SOP is the most universal form of description of expert research process for solving a typical task and can be used when creating a standard methodology. The focus of the management system of an accredited forensic laboratory on the quality of the results and the relationship with the internal structure of the SOP of forensic methodology is shown. The form of the registration passport of a forensic expert methodology in accordance with the requirements of the management system is described.
Research on the construction process of human resources supply chain of small and micro enterprises based on Internet of Things technology

The Internet of Things (IoT) helps establish massive viewpoints and complicated yet functional operational systems. IoT can strengthen the entire supply chain. However, due to a larger integration gap between HRM and SCM and fuzzy, integration of these two contemporary issues has been severely delayed even if academics in each field continue to progress SCM and HRM contribution to creating greater sustainable firms. The findings of the study show that the fuzzy based HRM-SCM strategy is workable and points to the educational requirement as the HRM element with the most positive influence. Designing and assessing the network of a supply chain is a crucial and challenging decision. In order to establish a comprehensive and synergistic foundation for the HRM-SCM relationship, this paper proposes a research program for this integration. The IoT is used in this study to maintain the supply-chain analytics for HRM. Additionally, the Butter Ant Optimization (BAO) is suggested to improve SCM performance. The IoT+FL+BAO approach is the one that this study suggests using. In comparison to other previous approaches, the experimental results show that the proposed IoT+FL+BAO approach improves the supply-chain performance proportion by 97.9%, the efficiency proportion by 98.2%, employee satisfaction by 97.03%, decision-making scale by 97.2%, prediction rate by 96.5%, and F1-score by 98.4%.
Statistical processing of time slices of electroencephalography signals during brain reaction to visual stimuli

We studied the response of cerebral cortex to visual stimuli such as the photographs of human faces for detecting the event of self-identification and for the localization of brain structures responsible for it. In medical diagnostics and neurophysiology the process of image identification is of major interest. The time slices of electroencephalography (EEG) signals were processed with the newly developed statistical method based on a special way of generating data by means of time slices. This method identified the reaction of different regions of the cerebral cortex to visual stimuli in the standard 10–20% EEG electrode placement method. Additionally, the proposed method uses minima of the p-value functions instead of the p-value thresholds to identify maximum differentiation time points. We revealed the existence of a statistically significant difference between the reactions to different stimuli exhibited by different regions of the cerebral cortex that are responsible for visual identification. Index Terms—electroencephalography, EEG, statistical signal processing, brain reaction to visual stimuli, evoked potentials, variance analysis, parametric ANOVA statistical method, nonparametric Kruskal–Wallis statistical method.
Scalable Neural Network Algorithms for High Dimensional Data

The boundary for machine learning engineers lately has moved from the restricted data to the algorithms' failure to involve every one of the data in the time permitted. Due of this, scientists are presently worried about the adaptability of machine learning algorithms notwithstanding their exactness. The key to success for many computer vision and machine learning challenges is having big training sets. A few published systematic reviews were taken into account in this topic. Recent systematic reviews may include both more recent and older research on the subject under study. Thus, the publications we examined were all recent. The review utilized information that were gathered somewhere in the range of 2010 and 2021. System: In this paper, we make a modified brain organization to eliminate possible components from extremely high layered datasets. Both a totaled level and an exceptionally fine-grained level of translation are feasible for these highlights. It is basically as easy to grasp non-straight connections as it is a direct relapse. We utilize the method on a dataset for item returns in web based shopping that has 15,555 aspects and 5,659,676 all out exchanges. Result and conclusion: We compare 87 various models to show that our approach not only produces higher predicted accuracy than existing techniques, but is also interpretable. The outcomes show that feature selection is a useful strategy for enhancing scalability. The method is sufficiently abstract to be used with many different analytics datasets
Socio-Technical Dialogue and Linguistic Interaction. Intelligent Personal Assistants (IPA) in the Private Home

Abstract The article addresses the phenomenon of voice-controlled assistance systems, or more precisely: smart speakers with voice user interfaces, as they have been finding their way into private households for some time. The public discourse revolves, among other things, around the question of the extent to which the socio-technical exchange with such systems differs from social interaction or increasingly approaches it, and, related to this, whether individual users must adapt to machines or vice versa. At the same time, empirical research shows that socio-technical dialogues and social interaction are often not two separate worlds, but components of a complex practice in which prima facie ontologically different participants are involved. These circumstances make it possible to reconstruct the characteristics of both socio-technical dialogue and the social interaction which is situated around it as linguistically mediated performances from a praxeological perspective. In this context, systematic transitions between socio-technical dialogue and a “meta-interaction space” play an important role, as do various forms of “sequential” processes and their mediation with each other. This article approaches these phenomena based on empirical data from an ongoing research project, notably audio-visual recordings of situations of the initial installation/commissioning of IPA in two- and multi-person households, audio recordings of situations of everyday use, log data of socio-technical dialogue which the systems make available to the users through the corresponding smartphone app.
PAC-learning with approximate predictors

Abstract Approximate learning machines have become popular in the era of small devices, including quantised, factorised, hashed, or otherwise compressed predictors, and the quest to explain and guarantee good generalisation abilities for such methods has just begun. In this paper, we study the role of approximability in learning, both in the full precision and the approximated settings. We do this through a notion of sensitivity of predictors to the action of the approximation operator at hand. We prove upper bounds on the generalisation of such predictors, yielding the following main findings, for any PAC-learnable class and any given approximation operator: (1) We show that under mild conditions, approximable target concepts are learnable from a smaller labelled sample, provided sufficient unlabelled data; (2) We give algorithms that guarantee a good predictor whose approximation also enjoys the same generalisation guarantees; (3) We highlight natural examples of structure in the class of sensitivities, which reduce, and possibly even eliminate the otherwise abundant requirement of additional unlabelled data, and henceforth shed new light onto what makes one problem instance easier to learn than another. These results embed the scope of modern model-compression approaches into the general goal of statistical learning theory, which in return suggests appropriate algorithms through minimising uniform bounds.