Reviewerly/exHarmony huggingface dataset:
False Media Detection by Using Deep Learning

Peoples meet across numerous images and videos in everyday life, and most of them are digitally manipulated in some way to some extent. Most of these images are subjected to simple Instagram effects or have undergone photoshop to completely transform the original picture. Since humans are not good enough to recognize the originality of these morphed images so they might be exposed to wrong messages or content which may be disastrous at individual, community level in some aspect. These morphed contents are called deep fakes. The illegal use of these deep fakes is prevalent worldwide to get political advantage. So, the main objective of this chapter is to detect any kind of transformation/effects in an image that creates questions about the originality of its content. As for most of the human pictures, videos and images are trustworthy sources of information and at the same time the internet is flooded with morphed images and videos, created by face re-enactment algorithms. The proposed detection algorithm is efficient enough and has a good degree of accuracy to detect any kind of transformation in a given picture or video.
FEATURES OF PERSONAL WELL-BEING OF MILITARIES WHO WERE IN THE ATO ZONE

In Ukrainian military psychology, the emphasis is on mitigating the effects of post-traumatic stress on servicemen. Little attention is paid to the prevention and study of the positive functioning of combatants. In the study of personal well-being of servicemen, its psychological aspect is presented, social and emotional well-being was less studied. Personal well-being is seen as an integral personal formation that provides a person with the conditions for positive actions and experiencing a successful relationship. The results of an empirical study of the personal wellbeing of ATO participants are presented (N = 107). It is shown that all components of personal well-being: emotional, social and psychological well-being have a higher level in servicemen who were in the antiterrorist operation zone, compared to non-military men. Satisfaction with life of anti-terrorist operation participants is more related to their own assessment of achievements, personal characteristics and actions, while non-combatants’ satisfaction with life depends more on assessments of the social environment. Achieving individual goals is more important for non-military. The goals of servicemen are more socially oriented, they feel the social value of their activities, and therefore see more meaning in their lives. The criterion for assessing the personal success of the participants of the anti-terrorist operation is only their own feelings; they tend to pay less attention to social approval. The obtained data are relevant for the Chernihiv region.
Improving students’ vocabulary mastery through picture cards

This study aims to improve the vocabulary mastery of VII grade students of SMP Negeri 4 Taniwel, West Seram, Mollucas, through the use of picture cards. This research was classroom action research. The research subjects were grade VII students of SMP Negeri 4 Taniwel, West Seram, Mollucas, consisting of 24 students. The techniques for collecting the data were observation and interview, whereas the instruments for collecting data were observation checklists, field notes, and tests. The data analysis using qualitative analysis and quantitative analysis. The qualitative analysis was related to the result analysis of the observation, interview, field notes, and observation checklist, while the quantitative analysis was related to the analysis process of simple statistics from the students’ test results. The result of the research indicates that the use of picture cards can improve the students’ vocabulary mastery, as shown by the students’ test results that improved in each cycle. The students’ vocabulary mastery was said “improved” if their percentage of completeness overall achieved the indicator of success ≥85%. In the first cycle, the percentage of the students’ completeness overall improved to 75% with an average score of 74.1 but did not achieve the indicator of success. Therefore, the cycle was repeated in the second cycle with some improvements. In the second cycle, the percentage of the students’ completeness overall achieved the indicator of success 91.6% or ≥85%. It meant that the student's vocabulary mastery has improved in the second cycle, and the cycle was ended.
Critical assessment of success factors of mass collaboration: a case of Jordanian retail companies

Purpose This study aims to carry out the critical assessment of success factors of mass collaboration in the context of retail companies operating in Jordan. Design/methodology/approach This study is quantitative in nature in which data has been gathered from the primary sources of information through a survey questionnaire. The employees and managers from the retail sector of Jordan have been considered. Confirmatory factor analysis has been carried out to determine the reliability and validity of the factors that are included in the research model. Quality of model and predictive prevalence has been tested with the help of determining the R-square of the model. Path assessment has also been done as a part of data analysis to assess the statistical significance of the association between different factors and the success of a mass collaboration. Findings The analysis determined that organizational structure, the mechanism in collaborative learning along with technologies adopted in collaborative learning, evaluation of learner’s performance and quality of knowledge has a significant effect on mass collaboration within the context of the retail sector in Jordan. Research limitations/implications The context of this research is the Jordanian retail sector; hence, it is very concise and cannot be necessarily applied within the context of other industries. This research has adopted a quantitative design where the data was collected with the help of a survey questionnaire based on the close-ended questionnaire. This has limited the answers to the available options given. Originality/value The originality of this study has been ensured by carrying out a primary survey with the employees and managers belonging to the Jordanian retail sector to the promising approach to create agile knowledge and share to enhance the organizational context.
Using Ontologies in Failure Analysis

Abstract Fault analysis is a complex task that requires engineers to perform various analyses to detect and localize physical defects in semiconductor devices. The process is knowledge intensive and must be precisely documented. In order to ensure unambiguous documentation, engineers must agree on a clearly defined terminology specifying methods, tools, physical faults and their electrical signatures among other things, and it must be stored in a way that is usable for both engineers and software. One possible solution to this challenge is to formalize domain knowledge as an ontology, a knowledge base designed to store terminological definitions. This paper discusses the development of an ontology for electronic device failure analysis that uses a logic-based representation. The latter ensures that terms are interpreted the same way by engineers and software systems, facilitating the automation of tasks such as text classification, information retrieval, and workflow verification.
Text mining based sentiment analysis using a novel deep learning approach

Leveraging text mining for sentiment analysis, and integrating text mining and deep learning are the main purposes of this paper. The presented study includes three main steps. At the first step, pre-processing such as tokenization, text cleaning, stop word, stemming, and text normalization has been utilized. Secondly, feature from review and tweets using Bag of Words (BOW) method and Term Frequency $_$Inverse Document Frequency is extracted. Finally, deep learning by dense neural networks is used for classification. This research throws light on understanding the basic concepts of sentiment analysis and then showcases a model which performs deep learning for classification for a movie review and airline$_$ sentiment data set. The performance measure in terms of precision, recall, F1-measure and accuracy were calculated. Based on the results, the proposed method achieved an accuracy of $95.38%$ and $93.84%$ for a movie review and Airline$_$ sentiment, respectively.
Pengembangan Media Neraca Lengan untuk Memfasilitasi Kemampuan Berpikir Logis Anak Usia Dini

This research is motivated by the problems in the field regarding the limited availability of learning media in facilitating early childhood logical thinking skills, especially recognizing differences in objects based on the size of the object's weight. The importance of the ability to think logically from an early age to prepare children to live their daily lives is inseparable from logical thinking. The purpose of this study is to develop learning media to facilitate early childhood analytical thinking skills. The method used in this research is the development method with the Educational Design Research (EDR) model of the McKenney and Reeves model, which has three stages of research: analysis and exploration, design and construction, and evaluation and reflection. Data collection techniques used in this study were interviews, observation, questionnaires, and expert validation. The products that have been developed are then validated by media experts, material experts, and educational experts and then revised by researchers according to the validator's suggestions. Products that have been declared eligible to be tested based on the results of expert validation then tried twice on a limited basis on group B children in TK PGRI Karangmekar. The results of this study indicate that the developed media, namely balance scale media to facilitate early childhood logical thinking skills, is suitable for use in learning according to the results of expert validation, teacher responses, and student's ability to use balance scale media in learning in a limited trial.
On The Precision Of Markerless 3d Semantic Features: An Experimental Study On Violin Playing

Human motion analysis is an essential task in several domains and, depending on the application field, it requires different level of accuracy. In the motor control field it is commonly performed with motion capture systems and infrared markers that guarantee a high accuracy. However, these systems are expensive, cumbersome, and may induce bias. An alternative to marker-based technologies are image-based marker-less systems, that are cheaper and do not affect the naturalness of the motion. Although their accuracy level seems to limit their use in motor control field, a thorough quantitative comparison with marker-based techniques does not appear to be available yet. We compare the estimates of a 3D image-based marker-less pipeline we propose, with a standard marker-based system; the analysis is carried out on a multi-sensor dataset acquired to study the motion of violin players. The results we obtain on the precision level are suggesting that marker-less systems may successfully track performances in real-world settings.
Continuous User Authentication for Human-Robot Collaboration

Human-robot collaboration is on the increase and having a major impact on areas such as manufacturing, where the abilities of the human worker, augmented by those of the robot, bring increased flexibility and performance. However, close collaboration, including physical interaction, brings with it complex safety and security issues that were previously mitigated by human-robot segregation and isolated control networks. Exoskeletons pose a particularly interesting case whereby physical coupling of the user and robot is required throughout operation. We envisage the use of continuous authentication to exoskeletons, i.e. to ensure a user is who they claim to be, and that they have sufficient authority to operate the device for the duration of its use. In this paper we demonstrate such an approach to behavioural biometrics using data acquired through wearable sensors (hand manipulations recorded by a sensorised glove) while the user performs a selection of industrial tasks, including handling loads and inserting screws. The results show that the approach can discriminate between users with a low Equal Error Rate (EER; <3% in the worst case analysed). We believe that such an approach will also benefit other applications where wearables are used in robot control, such as in tele-operation.
Stochastic Cluster Embedding

Neighbor Embedding (NE) aims to preserve pairwise similarities between data items and has been shown to yield an effective principle for data visualization. However, even the best existing NE methods such as Stochastic Neighbor Embedding (SNE) may leave large-scale patterns hidden, for example clusters, despite strong signals being present in the data. To address this, we propose a new cluster visualization method based on the Neighbor Embedding principle. We first present a family of Neighbor Embedding methods that generalizes SNE by using non-normalized Kullback-Leibler divergence with a scale parameter. In this family, much better cluster visualizations often appear with a parameter value different from the one corresponding to SNE. We also develop an efficient software that employs asynchronous stochastic block coordinate descent to optimize the new family of objective functions. Our experimental results demonstrate that the method consistently and substantially improves the visualization of data clusters compared with the state-of-the-art NE approaches.
The Application of Tri-N in Genre-Based Indonesian Textbook for Junior High School

Learning Indonesian in K13 is communicative, CLIL, scientific, and genre-based. Indonesian material in junior high school is integrated in one genre. Textbooks as a learning tool compiled nationally consist of student books and teacher books. Bloom revised taxonomy, the highest ability is to create / creativity. Ki Hadjar Dewantara has long coined Tri-N (niteni, nirokke, nambahi) which is relevant to the scientific approach and the process of creating a revised edition of Bloom. Textbooks as a principal guide for teachers and students in learning must apply a scientific approach. Tamansiswa teachings are known as Tri-N. Niteni is the process of marking by paying close attention, using all the senses, digging deeper information, and reasoning by connecting knowledge already possessed from excavated products. Nirokke is imitating what is taught through a model / example / example from the teacher / learning resource by involving the mind, sensing, feeling / conscience and spirituality integrally and harmoniously. Nambahi is the process of adding to what he has learned in developing his creativity and ideas through the activities of designing, creating, and improvising. This study aims to describe the application of the niteni, nirokke, and supplementary processes in genre-based Indonesian SMP textbooks. The research method used is descriptive qualitative analysis of interactive models Miles Huberman. The genres representing this research are description, explanation, procedure, exposition, and narrative genres. The results showed that in these genres the process of niteni, nirokke, and nambahi always appeared.
Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics

Quality assessment is an important aspect in a variety of application areas. In this work, the objective quality assessment of 2.5D prints was performed. The work is done on camera captures under both diffuse (single-shot) and directional (multiple-shot) illumination. Current state-of-the-art 2D full-reference image quality metrics were used to predict the quality of 2.5D prints. The results showed that the selected metrics can detect differences between the prints as well as between a print and its 2D reference image. Moreover, the metrics better detected differences in the multiple-shot set-up captures than in the single-shot set-up ones. Although the results are based on a limited number of images, they show existing metrics’ ability to work with 2.5D prints under limited conditions.
A Runtime Safety Enforcement Approach by Monitoring and Adaptation

The use of models and formal analysis techniques at runtime is fundamental to address safety assurance during the system operational stage, when all relevant uncertainties and unknowns can be resolved. This paper presents a novel approach to runtime safety enforcement of software systems based on the MAPE-K control loop architecture for system monitoring and control, and on the Abstract State Machine as runtime model representing the enforcement strategy aimed at preserving or eventually restoring safety. The enforcer software is designed as an autonomic manager that wraps around the software system to monitor and manage unsafe system changes using probing and effecting interfaces provided by the system, so realising grey-box safety enforcement. The proposed approach is supported by a component framework that is here illustrated by means of a case study in the health-care domain.
An Attention Module for Convolutional Neural Networks

Attention mechanism has been regarded as an advanced technique to capture long-range feature interactions and to boost the representation capability for convolutional neural networks. However, we found two ignored problems in current attentional activations-based models: the approximation problem and the insufficient capacity problem of the attention maps. To solve the two problems together, we initially propose an attention module for convolutional neural networks by developing an AW-convolution, where the shape of attention maps matches that of the weights rather than the activations. Our proposed attention module is a complementary method to previous attention-based schemes, such as those that apply the attention mechanism to explore the relationship between channel-wise and spatial features. Experiments on several datasets for image classification and object detection tasks show the effectiveness of our proposed attention module. In particular, our proposed attention module achieves 1.00% Top-1 accuracy improvement on ImageNet classification over a ResNet101 baseline and 0.63 COCO-style Average Precision improvement on the COCO object detection on top of a Faster R-CNN baseline with the backbone of ResNet101-FPN. When integrating with the previous attentional activations-based models, our proposed attention module can further increase their Top-1 accuracy on ImageNet classification by up to 0.57% and COCO-style Average Precision on the COCO object detection by up to 0.45. Code and pre-trained models will be publicly available.
Quantum coin flipping, qubit measurement, and generalized Fibonacci numbers

The problem of Hadamard quantum coin measurement in $$n$$ trials, with an arbitrary number of repeated consecutive last states, is formulated in terms of Fibonacci sequences for duplicated states, Tribonacci numbers for triplicated states, and $$N$$ -Bonacci numbers for arbitrary $$N$$ -plicated states. The probability formulas for arbitrary positions of repeated states are derived in terms of the Lucas and Fibonacci numbers. For a generic qubit coin, the formulas are expressed by the Fibonacci and more general, $$N$$ -Bonacci polynomials in qubit probabilities. The generating function for probabilities, the Golden Ratio limit of these probabilities, and the Shannon entropy for corresponding states are determined. Using a generalized Born rule and the universality of the $$n$$ -qubit measurement gate, we formulate the problem in terms of generic $$n$$ -qubit states and construct projection operators in a Hilbert space, constrained on the Fibonacci tree of the states. The results are generalized to qutrit and qudit coins described by generalized Fibonacci- $$N$$ -Bonacci sequences.
Heavy-tailed Streaming Statistical Estimation

We consider the task of heavy-tailed statistical estimation given streaming $p$-dimensional samples. This could also be viewed as stochastic optimization under heavy-tailed distributions, with an additional $O(p)$ space complexity constraint. We design a clipped stochastic gradient descent algorithm and provide an improved analysis, under a more nuanced condition on the noise of the stochastic gradients, which we show is critical when analyzing stochastic optimization problems arising from general statistical estimation problems. Our results guarantee convergence not just in expectation but with exponential concentration, and moreover does so using $O(1)$ batch size. We provide consequences of our results for mean estimation and linear regression. Finally, we provide empirical corroboration of our results and algorithms via synthetic experiments for mean estimation and linear regression.
StarVQA: Space-Time Attention for Video Quality Assessment

The attention mechanism is blooming in computer vision nowadays. However, its application to video quality assessment (VQA) has not been reported. Evaluating the quality of in-the-wild videos is challenging due to the unknown of pristine reference and shooting distortion. This paper presents a novel \underline{s}pace-\underline{t}ime \underline{a}ttention network fo\underline{r} the \underline{VQA} problem, named StarVQA. StarVQA builds a Transformer by alternately concatenating the divided space-time attention. To adapt the Transformer architecture for training, StarVQA designs a vectorized regression loss by encoding the mean opinion score (MOS) to the probability vector and embedding a special vectorized label token as the learnable variable. To capture the long-range spatiotemporal dependencies of a video sequence, StarVQA encodes the space-time position information of each patch to the input of the Transformer. Various experiments are conducted on the de-facto in-the-wild video datasets, including LIVE-VQC, KoNViD-1k, LSVQ, and LSVQ-1080p. Experimental results demonstrate the superiority of the proposed StarVQA over the state-of-the-art. Code and model will be available at: https://github.com/DVL/StarVQA.
Designing for a future self: how the architect Stéphane Beel empathises with wheelchair users

Architects tend to design following their own values and concerns. As such, designing for imaginary users, especially disabled ones, may pose major challenges. Drawing on aspects of focused ethnography, we show how the architect Stéphane Beel’s anticipation of using a wheelchair in the future due to a progressive-regressive disease influences his willingness and ability to empathise with, and design for, wheelchair users. We study a museum building in Belgium that he, as part of a larger design team, helped to restore, renovate, and expand, paying special attention to (wheelchair) accessibility. We rely on interviews, observations, design documents, and two tours guided by Stéphane. Our findings suggest that Stéphane’s ability to empathise with users benefits from his flexible use of his embodied self, and his moving between positions in various design stages. Foregrounding bodily resonances with wheelchair users seems to facilitate incorporating the other as self and reaching affect. At the same time, thinking of the self as other promotes looking at designs through someone else’s eyes. Empathy brought Stéphane to design inclusive environments, not because he has embodied knowledge derived from his use of a wheelchair or his technical knowledge as an architect, but because he feels affected by his own future possibility. Set against indifference, this affect triggers a powerful response in his architecture.