Reviewerly/exHarmony huggingface dataset:
Detection of Human Facial Expression using CNN Model

Facial expression is the most effective and herbal non verbal emotional conversation method People can range indoors the way they display their expressions Even pics of the same character within the identical countenance can vary in brightness historical past and pose and these variations are emphasized if thinking about particular subjects because of versions in shape ethnicity amongst others Hence countenance recognition remains a challenging trouble in PC vision To advise a solution for expression reputation that uses a combination of Convolutional Neural Network and precise picture prepossessing steps It defined the modern-day solution that has green facial capabilities and deep gaining knowledge of with convolutional neural networks CNN's has achieved high-quality success within the classification of assorted face emotions like glad angry unhappy and impartial Hundreds of neuron smart and layer smart visualization techniques have been applied the usage of a CNN informed with a publicly to be had photo data set So it’s positioned that neural networks can capture the colors and textures of lesions unique to respective emotion upon analysis which resembles human desire making.
Examining Cross-lingual Contextual Embeddings with Orthogonal Structural Probes

State-of-the-art contextual embeddings are obtained from large language models available only for a few languages.For others, we need to learn representations using a multilingual model.There is an ongoing debate on whether multilingual embeddings can be aligned in a space shared across many languages.The novel Orthogonal Structural Probe (Limisiewicz and Mareček, 2021) allows us to answer this question for specific linguistic features and learn a projection based only on mono-lingual annotated datasets.We evaluate syntactic (UD) and lexical (WordNet) structural information encoded in MBERT's contextual representations for nine diverse languages. 1 We observe that for languages closely related to English, no transformation is needed.The evaluated information is encoded in a shared cross-lingual embedding space.For other languages, it is beneficial to apply orthogonal transformation learned separately for each language.We successfully apply our findings to zero-shot and few-shot cross-lingual parsing.
Literary myth and journalistic logos: from contrast to integration

The relevance of the study of productive and creative exchange between literature and journalism is determined by the problematic relation between the two paradigms of the interpretation of reality. The paper covers the opposition and diffusion of literature and journalism. With polarising initiatives, these forms of verbal interpretation of reality have achieved the synergistic effect of their creative integration during their long genesis. The purpose of the study is to identify the conditions for achieving a fruitful synthesis of literary cyclicality and journalistic linearity, which achieves a synergistic effect. Furthermore, the purpose of the study is to clarify the contrast between the cyclicality of the literary image and the linearity of journalistic fact, which underlie the correlation between fiction and non-fiction. The methodological framework of the research includes the principles of historicism, objectivity, consistency, concreteness, methods of source analysis, methods of historical deduction and induction. Based on the results of the study, new scientific data were obtained on the historical specifics of the mutual influence of fine literature on public logos and the reverse interaction of journalistic topicality on the literary canon. The conclusions of the research can be used in the development of new humanitarian solutions in effective dialogue between artistic and literary fiction and actual journalistic factuality.
Hazardous and toxic waste standard design for temporary storage for oil and gas

One of the main activities in the oil and gas industry is the maintenance of diesel engines.There is a special process called preventive maintenance with regular oil changes.The process of changing oil is done regularly which results in B3 waste in the form of waste oil.Considering these aspects, the purpose of this research is to identify the temporary storage of petroleum waste for oil and gas based on the literature and the design of the B3 Temporary Waste Storage (TPS).The method used is descriptive qualitative methodology.The data refers to government regulations that have been made in order to create a safe, solicitous and orderly life.The results of these calculations are used as a reference in the design of the TPS, where the maximum waste capacity for the B3 Solid Waste Warehouse is 4.000 tons and for the B3 Liquid Waste Warehouse is 4.000 liters, and the building area of the B3 waste TPS is 108 m 2 .The layout design for new B3 waste is done based on suitability of characteristics B3 waste with a suitable building design (may combined in placing packaging waste).
INCELS ON REDDIT: A STUDY IN SOCIAL NORMS AND DECENTRALISED MODERATION

The social news website Reddit has a long history of hosting communities (‘subreddits’) that advocate or encourage white supremacy (Gillespie 2018), disparagement of minority groups (Topinka 2017), and violence against women (Massanari 2017). As a platform that relies heavily on volunteer moderators to self-govern the subreddits (Matias 2016), Reddit has been criticised for failing to adequately enforce its site-wide rules (Gillespie 2018). Incels—an internet subculture that ascribes to deeply misogynistic beliefs—grew in visibility when they developed subreddits on Reddit. After ongoing criticism and media attention about harmful behaviour of incels both on and off the platform, Reddit imposed escalating sanctions and ultimately banned the most visible of these subreddits over a period of several years. In this paper, we focus on the interaction between formal rules and social norms in incel and related subreddits. This paper aims to improve understanding about how problematic norms are contested in (partially-) decentralised systems of content moderation. We examine discourse about moderation to better understand the role of moderation teams in maintaining and changing social norms in their communities and to examine the interaction between these norms and both sitewide and subreddit-specific rules. Our analysis suggests that the threat of prohibition alone is unlikely to be sufficient to drive cultural change in problematic subreddits. We argue that content moderation is an insufficient frame to understand the regulation of harmful communities; real change requires addressing the underlying cultural norms rather than focusing on individual pieces of content.
Implicit Premise Generation with Discourse-aware Commonsense Knowledge Models

Enthymemes are defined as arguments where a premise or conclusion is left implicit.We tackle the task of generating the implicit premise in an enthymeme, which requires not only an understanding of the stated conclusion and premise, but also additional inferences that could depend on commonsense knowledge.The largest available dataset for enthymemes (Habernal et al., 2018) consists of 1.7k samples, which is not large enough to train a neural text generation model.To address this issue, we take advantage of a similar task and dataset: Abductive reasoning in narrative text (Bhagavatula et al., 2020).However, we show that simply using a state-of-the-art seq2seq model fine-tuned on this data might not generate meaningful implicit premises associated with the given enthymemes.We demonstrate that encoding discourse-aware commonsense during fine-tuning improves the quality of the generated implicit premises and outperforms all other baselines both in automatic and human evaluations on three different datasets.
Fuzzy Partitions and Multi-Scale Representation of 1D Data

We analyze various multi-scale representations of a one-dimensional signal in spaces with a closeness relation determined by a symmetric and positive semi-definite kernel.We discuss other kernels than the predominant Gaussian kernel in a scale space representation of a one-dimensional signal.We show that kernels arising from generating functions of fuzzy partitions can be treated with the same success.We also show that the reconstruction from the proposed multi-scale representations of better quality than the reconstruction from MLP with almost double the number of neurons in 4 hidden layers.
From noisy point clouds to complete ear shapes: unsupervised pipeline

Ears are a particularly difficult region of the human face to model, not only due to the non-rigid deformations existing between shapes but also to the challenges in processing the retrieved data. The first step towards obtaining a good model is to have complete scans in correspondence, but these usually present a higher amount of occlusions, noise and outliers when compared to most face regions, thus requiring a specific procedure. Therefore, we propose a complete pipeline taking as input unordered 3D point clouds with the aforementioned problems, and producing as output a dataset in correspondence, with completion of the missing data. We provide a comparison of several state-of-the-art registration and shape completion methods, concluding on the best choice for each of the steps.
A Clustering-Guided Integer Brain Storm Optimizer for Feature Selection in High-Dimensional Data

For high-dimensional data with a large number of redundant features, existing feature selection algorithms still have the problem of “curse of dimensionality.” In view of this, the paper studies a new two-phase evolutionary feature selection algorithm, called clustering-guided integer brain storm optimization algorithm (IBSO-C). In the first phase, an importance-guided feature clustering method is proposed to group similar features, so that the search space in the second phase can be reduced obviously. The second phase applies oneself to finding optimal feature subset by using an improved integer brain storm optimization. Moreover, a new encoding strategy and a time-varying integer update method for individuals are proposed to improve the search performance of brain storm optimization in the second phase. Since the number of feature clusters is far smaller than the size of original features, IBSO-C can find an optimal feature subset fast. Compared with several existing algorithms on some real-world datasets, experimental results show that IBSO-C can find feature subset with high classification accuracy at less computation cost.
Growing Student's Learning Interest Through the Use of Integrated E-Learning Media Daily Activities

The purpose of this research is to find out how enthusiasm and interest in learning of students during a pandemic takes place. As is the case today, all forms of community activities are limited due to the Covid-19 virus pandemic, including in the field of education which is one of the main focuses of every nation in living life. Of course, a solution is needed to be able to overcome the problem of education in the midst of this pandemic. The problem that oft en occurs in education during a pandemic is the mechanism of teaching and learning activities. The solution given in overcoming this problem is the e-learning method of learning . The e-learning method of learning is an educational system or concept that utilizes information technology in the teaching and learning process. This research was conducted using a literature study research method, which examines various reference sources, which are obtained from journals or scientific articles, both from print and online media. The results of this study are expected to increase the awareness of students in learning, even though using the e-leaning method, and so that students can adapt to the technology used
Effect of Extension Training on Technical Efficiency of Maize Farmers in Ogbomoso Agricultural Zone of Oyo State, Nigeria

The efficiency of any entrepreneur is a function of personal production skill, knowledge and experience acquired over time and exposure via training in the field of his or her enterprise focus. It is on this premise that this study examined the effect of extension training on technical efficiency of maize farmers in Ogbomoso Agricultural Zone, Oyo State, Nigeria. Multistage sampling procedure was adopted for the selection of One-hundred and Eighty-One (181) maize farmers, while structured and validated interview schedule was used to obtain necessary information from the sampled respondents. Data collected were analyzed using both descriptive and inferential statistical tools. The mean age of sampled farmers was 48 years and majority (80.1%) of the farmers was male. The farmers with extension contact have access to different extension trainings on maize production in the study area. The average household sizes were 7 and 8, with the average farm sizes of 1.423ha and 1.417ha for both contact and non-contact maize farmers respectively. Those farmers with extension contact were found to be technically efficient than non-contact farmers. Significant relationship existed between some selected socio-economic characteristics of both sampled maize farmers and their maize output. The study therefore suggests the need to create more awareness on the roles of extension services on crop production among farmers and the authorities concern with extension service delivery should improve on the frequency of extension contact in order to encourage farmers' participation in extension activities in the study area and rural communities in Nigeria at large. Keywords: Extension training, technical efficiency, contact and non-contact maize farmers.
Unmanned Aerial Vehicle Path Planning using Bat Algorithm

Unmanned Aerial Vehicles (UAV) was introduced after World War II. In 1980’s UAV consider as important weapon system. Initially UAV needs initial position and target position. In this paper bat algorithm is proposed with mixed objective constraints which helps in directing the UAV. The process is initialized by generating the initial population of bat. Then by updating the population size and generation of bat the fitness value with minimum frequency is found that helps to avoid convergence among UAV. Finally the evaluation which gives minimum frequency is considered as optimal solution.
A High-Reliability Trust Management System in Cloud Computing

Cloud data storage is revolutionary because it eliminates the need for additional hardware, which is often costly, inconvenient, and requires additional space.Cloud data storage allows data owners to store large amounts of data in a flexible way and at low cost.The number of online cloud storage services and their consumers has therefore increased dramatically.However, ensuring the privacy and security of data on a digital platform is often a challenge.A cryptographic taskrole-based access control (T-RBAC) approach can be used to www.videleaf.comprotect data privacy.This approach ensures the accessibility of data for authorized consumers and keeps it safe from unauthorized consumers.However, this type of cryptographic approach does not address the issue of trust.In this paper, we propose a comprehensive trust model integrated with a cryptographic T-RBAC to enhance the privacy and security of data stored in cloud storage systems, and suggests that trust models involve inheritance and hierarchy in the roles and tasks of trustworthiness evaluation, where this study aims to identify the most feasible solution for the trust issue in T-RBAC approaches.Risk evaluations regarding other possible flaws of the design are also performed.The proposed design can decrease risk by providing high security for cloud storage systems and improve the quality of decisions of cloud operators and data owners.
Analog Fotoğraftan Dijital Fotoğrafa Geçişte Fotoğrafik Gerçekliğin Dönüşümü

After the invention of the camera, it became questionable whether the photographic technology represented reality or to what extent it represented reality.The first period of reality debates focused on mainly about the replacement of reality with elements such lens selection, depth of field, exposure, angle, framing and composition, while recent reality debates are about the disappearance of human qualities (illusion, idea, consciousness, image, desire, scene, relationship).Again, the current debates that in the digital image era, photography has disappeared, that the image cannot understand digital images, that the subjective aspect of reality has been destroyed, and that there has been a different visual image era that cannot be called a photo.While the concepts of natural magic, illusion, thinking, appearing and disappearing gained during the analogue period of the photograph refer to the reality of the photograph and give the photograph a unique form, the ready-made codes loaded on the camera before the photographing process; manipulations and algorithmic processes during shooting have eliminated the reality of the photograph.In this study, the notion of reality was put into a conceptual framework and definition and the transformation of photographic reality with the transition from analogue photography to digital photography was tried to be explained and literature review method was used.In this study, the concepts such as aura (halo), spectra, studium, punctum, which are used to explain photographic reality are discussed in the context of analog and digital photography and the human aspect of reality is aimed to be revealed.
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents

Document-level neural machine translation (DocNMT) achieves coherent translations by incorporating cross-sentence context.However, for most language pairs there's a shortage of parallel documents, although parallel sentences are readily available.In this paper, we study whether and how contextual modeling in DocNMT is transferable via multilingual modeling.We focus on the scenario of zero-shot transfer from teacher languages with document level data to student languages with no documents but sentence level data, and for the first time treat document-level translation as a transfer learning problem.Using simple concatenation-based DocNMT, we explore the effect of 3 factors on the transfer: the number of teacher languages with document level data, the balance between document and sentence level data at training, and the data condition of parallel documents (genuine vs. backtranslated).Our experiments on Europarl-7 and IWSLT-10 show the feasibility of multilingual transfer for DocNMT, particularly on document-specific metrics.We observe that more teacher languages and adequate data balance both contribute to better transfer quality.Surprisingly, the transfer is less sensitive to the data condition, where multilingual DocNMT delivers decent performance with either backtranslated or genuine document pairs.
Role-Playing Model on Speaking Skill for Elementary School Students

The lack of creativity and skills to speak Bahasa Indonesia in grade IV teachers in some elementary schools directly impacts the learning process of Bahasa Indonesia in the classroom. The learning process of Bahasa Indonesia lessons was still delivered with a mixture of mother tongue or daily regional language. The purpose of this study was to explore the significant influence of role-playing models on Indonesian language speaking skills in grade IV elementary school. This type of research was pseudo-experimental research (quasi-experimental) using a quantitative approach. The population in this study was the entire fourth grade of elementary school consisting of 7 elementary schools. The sampling process used the purposive sampling technique, which meant selecting experimental and control classes—the method of extracting data with field observations and live interviews. The results showed on the variable Indonesian language speaking skills; students obtained a value of Fcount was 81,352 and p = 0,000. So it can be concluded that there is a significant influence on applying role-playing type cooperative learning to the Bahasa speaking skills of grade IV elementary school students. The findings of this study will have implications for improving students' Bahasa speaking skills.
Learning Videos to Improving Students’ Reading Comprehension in Elementary School

Learning to read during learning activities from home is less effective because reading learning media are difficult to find. This study aims to create a reading learning video development for first-grade elementary school students and develop a reading learning video for first-grade elementary school students whose validity is tested using the ADDIE model. However, due to limited time and resources, the implementation and evaluation stages were not carried out. Thus, only the analysis, design, and development stages are carried out. The data collection of this study used a questionnaire method by providing assessment sheets to two media expert lecturers, two Indonesian language material experts, and two first-grade elementary school teachers. The data from the validation results from the experts were then analysed using the mean formula to determine the average score of the validity of the reading learning videos. The analysis results showed that the average score of the validation of the reading learning video was 4.73, and the validation of the material was 4.81. Based on the results of this analysis, the reading learning video for first-grade students of elementary school was declared valid and had very good criteria. The trial was not carried out on students due to the Covid-19 pandemic. Therefore, it is suggested that other researchers carry out further research to analyze the effectiveness of the instructional videos.
Artificial Eyes with Emotion and Light Responsive Pupils for Realistic Humanoid Robots

This study employs a novel 3D engineered robotic eye system with dielectric elastomer actuator (DEA) pupils and a 3D sculpted and colourised gelatin iris membrane to replicate the appearance and materiality of the human eye. A camera system for facial expression analysis (FEA) was installed in the left eye, and a photo-resistor for measuring light frequencies in the right. Unlike previous prototypes, this configuration permits the robotic eyes to respond to both light and emotion proximal to a human eye. A series of experiments were undertaken using a pupil tracking headset to monitor test subjects when observing positive and negative video stimuli. A second test measured pupil dilation ranges to high and low light frequencies using a high-powered artificial light. This data was converted into a series of algorithms for servomotor triangulation to control the photosensitive and emotive pupil dilation sequences. The robotic eyes were evaluated against the pupillometric data and video feeds of the human eyes to determine operational accuracy. Finally, the dilating robotic eye system was installed in a realistic humanoid robot (RHR) and comparatively evaluated in a human-robot interaction (HRI) experiment. The results of this study show that the robotic eyes can emulate the average pupil reflex of the human eye under typical light conditions and to positive and negative emotive stimuli. However, the results of the HRI experiment indicate that replicating natural eye contact behaviour was more significant than emulating pupil dilation.