Reviewerly/exHarmony huggingface dataset:
An Investigation on Facial Emotional Expression Recognition Based on Linear-Decision-Boundaries Classifiers Using Convolutional Neural Network for Feature Extraction

This paper presents an investigation study on facial emotional expression recognition. Three linear-decision-boundaries classifiers: linear support vector classification (LSVC), linear discriminant analysis (LDA) and softmax (SM) techniques are utilized based on convolutional neural network (CNN) for efficient feature extraction, namely CNN-LSVC, CNN-LDA and CNN-SM respectively. Hyper-parameter tuning or selection needs the least effort for such three linear-decision-boundaries classifiers. In order to enhance recognition performance, particular image preprocessing: intensity transformation as well as image cropping technique are implemented before feeding input images into CNN feature extraction. Relying on 10-fold cross validation of 80%-20% training-testing CK+ dataset, above 90% average results of precision, recall, F1 scores and accuracy rates are yielded by all such three investigated methods. Confusion matrix is also determined for more-detail of results examination.
Towards Developing an Intelligent Wheelchair for People with Congenital Disabilities and Mobility Impairment

Physical disability in people may be either congenital or acquired. In either cases, the disable person requires an aid for movement in both indoor and outdoor environment. The most common form of assistance to provide support to the disabled is the wheelchair. In this research paper, we are presenting our prototyped and developed wheelchair that is designed to provide assistance to various types of mobility impairments. It has the provision for both - voice commands as well manual controls via a joystick. People suffering from various motor disorders are incapacitated and thus, need to use other ways to control the wheelchair. For this reason, the wheelchair includes customizable voice control system which is Bengali by default. Along with the modes of controls, the wheel chair incorporates a feature that can be used for asking help in case of a necessity. Thus informing a relative or caregiver has been simplified which notifies them if the user is in need. Along with this, the provision of continuous monitoring is also present for ease of caregivers to locate the user when help is asked or the caregiver is unable to be physically present with the user. Other features such as obstacle detection, speed control on ramps, lighting up of the headlight in dark environment, reclining of the wheelchair has been integrated in the wheel chair as well. The prototype was demonstrated and evaluated in a controlled environment, The outcome of the experiments proved its effectiveness and efficiency for the focused users.
Linked Data Architecture for Assistance and Traceability in Smart Manufacturing

Traceability systems and digital assistance solutions are becoming increasingly vital parts of modern manufacturing environments. They help tracking quality-related information throughout the production process and support workers and maintenance personnel to cope with the increasing complexity of manufacturing technologies. In order to support these use cases, the integration of information from different data sources is required to create the necessary insights into processes, equipment and production quality. Common challenges for such integration scenarios are the various data formats, encodings and software interfaces that are involved in the acquisition, transmission, management and retrieval of relevant product and process data. This paper proposes a Linked Data based system architecture for modular and decoupled assistance software. Its web-oriented approach allows to connect two usually disparate data sets: semantic descriptions of complex production systems on the one hand and high-volume and high-velocity production data on the other hand. The proposed concept is illustrated with a typical example from the manufacturing domain. The described End-of-Line quality assessment on forming machines is used for traceability and product monitoring.
Social practice on Facebook: Critical discourse analysis in the process of text production

The study aims to identify the ways to produce text production process by Universitas Islam Negeri (UIN, or State Islamic University) students in Padang on Facebook. Documentations, observations, and in depth-interviews were used to collect data. There were 1,214 discourses found on group and personal accounts of 27 informants, and 400 discourses were taken as data of the research. The analysis was conducted by following Fairclough’s Critical Discourse Analysis, (CDA), with the Critical Linguistics approach. The research findings show that the text production process by UIN students on Facebook were done in three ways, namely producing their own text, spreading other people’s texts that are shared from the site, and producing text as a result of consumption of other texts. Producing text itself is a way of producing text by creating its own status as a form of expression of thoughts, feelings, and experiences, without referring to other texts or texts that have been published on other walls. The form of production by spreading text from other website is the most common form of text production. Production status is a form of the author’s reaction to the text he or she understood.
Introduction to Jupyter Notebooks

Jupyter notebooks provide an environment where you can freely combine human-readable narrative with computer-readable code. This lesson describes how to install the Jupyter Notebook software, how to run and create Jupyter notebook files, and contexts where Jupyter notebooks can be particularly helpful.
Inferring Multi-Type Birth-Death Parameters for a Structured Host Population with Application to HIV Epidemic in Africa

Human Immunodeficiency Virus (HIV) dynamics in Africa are purely characterised by sparse sampling of DNA sequences for individuals who are infected.There are some sub-groups that are more at risk than the general population.These sub-groups have higher infectivity rates.We came up with a likelihood inference model of multi-type birth-death process that can be used to make inference for HIV epidemic in an African setting.We employ a likelihood inference that incorporates a probability of removal from infectious pool in the model.We have simulated trees and made parameter inference on the simulated trees as well as investigating whether the model distinguishes between heterogeneous and homogeneous dynamics.The model makes fairly good parameter inference.It distinguishes between heterogeneous and homogeneous dynamics well.Parameter estimation was also performed under sparse sampling scenario.We investigated whether trees obtained from a structured population are more balanced than those from a non-structured host population using tree statistics that measure tree balance and imbalance.Trees from non-structured population were more balanced basing on Colless and Sackin indices.
GENERATION Z: WAYS, METHODS AND FORMS OF TRAINING (TO THE QUESTION OF INCREASING THE MOTIVATION OF STUDENTS AND THE PROFESSIONAL GROWTH OF TEACHERS)

GENERATION Z: WAYS, METHODS AND FORMS OF TRAINING (TO THE QUESTION OF INCREASING THE MOTIVATION OF STUDENTS AND THE PROFESSIONAL GROWTH OF TEACHERS)
TANet: Robust 3D Object Detection from Point Clouds with Triple Attention

In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches. We observe two crucial phenomena: 1) the detection accuracy of the hard objects, e.g., Pedestrians, is unsatisfactory, 2) when adding additional noise points, the performance of existing approaches decreases rapidly. To alleviate these problems, a novel TANet is introduced in this paper, which mainly contains a Triple Attention (TA) module, and a Coarse-to-Fine Regression (CFR) module. By considering the channel-wise, point-wise and voxel-wise attention jointly, the TA module enhances the crucial information of the target while suppresses the unstable cloud points. Besides, the novel stacked TA further exploits the multi-level feature attention. In addition, the CFR module boosts the accuracy of localization without excessive computation cost. Experimental results on the validation set of KITTI dataset demonstrate that, in the challenging noisy cases, i.e., adding additional random noisy points around each object,the presented approach goes far beyond state-of-the-art approaches. Furthermore, for the 3D object detection task of the KITTI benchmark, our approach ranks the first place on Pedestrian class, by using the point clouds as the only input. The running speed is around 29 frames per second.
LOGIC WITH VECTOR SEMANTIC AS A MEANS OF KNOWLEDGE BASES VERIFICATION

The paper discusses the general issues of automated search of artifacts in rule-based knowledge bases (KB) based on logic with vector semantics in the VTF-logic variant. Cases of falsity of antecedent of rules at all admissible values of truth of input premises, existence of terms which are not used anywhere and generation of uncertain values of truth, emergence of contradictions are considered. Automation is considered as the organization of the direct attached logical inference opening artifacts of KB. The first two cases are identified by counting the number of each rule triggering and identifying terms that are not tied to the rules. The contradiction is revealed by the verification of the truth of the conclusions-hypotheses. The presence of a conclusion with truth (1; 1) (complete contradiction) signals a contradiction at one of the stages of reasoning, which is set by the back trace of the logical chain. A necessary stage of inference is to combine the evidence using 11-composition (the second form of disjunction). The paper also presents the principle of calculating the truth of the conclusion based on the truth of the premises, the strategy of combining evidence, numerical measures that can be used in the conclusion.
Bounds on Over-Parameterization for Guaranteed Existence of Descent Paths in Shallow ReLU Networks

We study the landscape of squared loss in neural networks with one-hidden layer and ReLU activation functions. Let $m$ and $d$ be the widths of hidden and input layers, respectively. We show that there exit poor local minima with positive curvature for some training sets of size $n\geq m+2d-2$. By positive curvature of a local minimum, we mean that within a small neighborhood the loss function is strictly increasing in all directions. Consequently, for such training sets, there are initialization of weights from which there is no descent path to global optima. It is known that for $n\le m$, there always exist descent to global optima from all initial weights. In this perspective, our results provide a somewhat sharp characterization of the over-parameterization required for existence of descent paths in the loss landscape.
Improving Generalization via Attribute Selection on Out-of-the-Box Data

Zero-shot learning (ZSL) aims to recognize unseen objects (test classes) given some other seen objects (training classes) by sharing information of attributes between different objects. Attributes are artificially annotated for objects and treated equally in recent ZSL tasks. However, some inferior attributes with poor predictability or poor discriminability may have negative impacts on the ZSL system performance. This letter first derives a generalization error bound for ZSL tasks. Our theoretical analysis verifies that selecting the subset of key attributes can improve the generalization performance of the original ZSL model, which uses all the attributes. Unfortunately, previous attribute selection methods have been conducted based on the seen data, and their selected attributes have poor generalization capability to the unseen data, which is unavailable in the training stage of ZSL tasks. Inspired by learning from pseudo-relevance feedback, this letter introduces out-of-the-box data—pseudo-data generated by an attribute-guided generative model—to mimic the unseen data. We then present an iterative attribute selection (IAS) strategy that iteratively selects key attributes based on the out-of-the-box data. Since the distribution of the generated out-of-the-box data is similar to that of the test data, the key attributes selected by IAS can be effectively generalized to test data. Extensive experiments demonstrate that IAS can significantly improve existing attribute-based ZSL methods and achieve state-of-the-art performance.
Calculation of the irradiance of solar radiation in a greenhouse with a complex structure using a diagram for sky view factor

The irradiance of solar radiation in a greenhouse with a complex structure was calculated using a diagram for sky view factor (DSVF) and compared with observed values. Structural materials (such as side pillars and rafters) and non-structural materials (such as lock channels, door frames, and ventilation fans) were treated as sunshine obstacles, including lower rafters and equal leg angles in the inner spaces of the greenhouse. Radiation directions simulated using a DSVF were used for calculating the irradiance of diffuse solar radiation. With respect to the irradiance of total solar radiation on a sunny day, the calculated values were found to be higher than the observed values early in the morning. However, the overall fluctuation due to diurnal change of the solar elevation and in the sharp drops due to the sunshine obstacles were reproduced. Furthermore, the diurnal changes of the irradiance of non-direct solar radiation on a sunny day and the irradiance of total and non-direct solar radiations on a cloudy day could also be reproduced. The relative error of the calculated irradiance of daytime (from 04:00 to 20:00) total and non-direct solar radiations to the measured value was 4.3% for the total radiation on the sunny day, 5.5% for the non-direct radiation on the sunny day, −1.1% for the total radiation on the cloudy day, and 0.3% for the non-direct radiation on the cloudy day. The results of this study indicated that the DSVF can be useful for calculating diffuse solar radiation.
The ALARA Model of Information Search on Websites

The emergence of the Internet has enabled many end-users to search for information themselves as there is practically immeasurable amount of information, often the owners of the website are not held responsible for the quantity and quality of information made available for their users to make an informed decision. The paper presents a novel methodology to explore understand how websites owners. The ALARA Model of Information search on Website is a new theoretical and methodological approach for understanding the quantity and quality of the information provided by Information Custodian. ALARA is an acronym that stands for Availability, Location, Accessibility, Relatability and Actionability. A detailed description of the methodological approach is presented. The paper ends with applicability options and the limitations of the model.
Medialities of participation in sound art

Audience participation is a strong thread running through much of the sound art practice all the way to its beginnings in the 1950s and 60s, yet it remains underexplored in the sound art discourse. At the same time, the kind of participation inherent to sound art diverges significantly from the ideas of participatory art and relational aesthetics dominating the contemporary art discourse as well. Sound art emphasizes the corporeal and cultural aspects of its medium; thus, the participatory processes it invokes are also media specific and inextricable from the material and structural aspects of the artwork. In this chapter I investigate the connection between mediality and participation in sound art, discussing the kinds of interactions that are made possible by sound artworks and mechanisms by which these interactions are facilitated and communicated to the participants. To do so, I apply the concept of affordance to a select number of artworks classified into three environment types: local, networked and augmented. I argue that the participatory affordances of sound artworks situated in each environment type exhibit certain similarities, showing how material and structural aspects of the artwork facilitate, affect and are affected by participatory processes.
Adversarial Representation Active Learning

Active learning aims to develop label-efficient algorithms by querying the most informative samples to be labeled by an oracle. The design of efficient training methods that require fewer labels is an important research direction that allows more effective use of computational and human resources for labeling and training deep neural networks. In this work, we demonstrate how we can use recent advances in deep generative models, to outperform the state-of-the-art in achieving the highest classification accuracy using as few labels as possible. Unlike previous approaches, our approach uses not only labeled images to train the classifier but also unlabeled images and generated images for co-training the whole model. Our experiments show that the proposed method significantly outperforms existing approaches in active learning on a wide range of datasets (MNIST, CIFAR-10, SVHN, CelebA, and ImageNet).
Strong Known Related-Key Attacks and the Security of ECDSA

The classical related-key attack (RKA) model fails to capture some real world systems that introduce related secret keys by design. In some blockchain applications, public keys are generated in a way that the corresponding secret keys are additively related. The difference between two secret keys are known to some third parties. In this paper, we propose the Strong Known Related-Key Attack (Strong KRKA) model to capture this scenario. ECDSA has long been considered to be inferior to Schnorr signature in terms of security, in sprite of its popularity in the standardization and real world usage. In this paper we show that Schnorr signature is not secure in the Strong KRKA model. In contrast, the security of ECDSA in the Strong KRKA model can be reduced to the unforgeability of ECDSA under chosen message attack. This theoretical result gives a different view of the relative security level of ECDSA and Schnorr, since ECDSA was developed in 1992.
Video Person Re-ID: Fantastic Techniques and Where to Find Them

The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest. The current status-quo solutions are based on attention neural models. In this paper, we propose Attention and CL loss, which is a hybrid of center and Online Soft Mining (OSM) loss added to the attention loss on top of a temporal attention-based neural network. The proposed loss function applied with bag-of-tricks for training surpasses the state of the art on the common person Re-ID datasets, MARS and PRID 2011. Our source code is publicly available on github.
Using BERT for Checking the Polarity of Movie Reviews

In this era, the data of the user on social media is generating in every millisecond.The importance of data can be noted or observed as these are the reviews, emotions, and opinions of the human being in the form of text.The customer-generated data can be related to events, food, products, etc.This information is a "key to success" for those who do business or are in government and other individuals.As the data is in the form of bulk so, it is evident that to analyze a content that is generated by a user must be complicated to manage as well as it must be time-consuming.For this, we need an intelligent system that helps us to figure out whether the content is positive, negative or neutral in the form of categories.This smart system commonly named sentiment analysis (SA), opinion mining (OM), subjectivity mining, etc. Opinion mining is the systematized mining approach.Through this, we can classify, thoughts and emotions from the text, speech, and databank sources through Natural Language Processing (NLP).The actual purpose of writing this paper is to determine the idea of human emotions with the help of BERT model, where we took a dataset of IMDB movie reviews, which are generated by a users' data.Our experimental methodology is adequate and robust, which in turn describes the quality of sentiment analysis.
Aplikasi Kahoot Sebagai Alat Pengujian Terhadap Pencapaian Murid Dalam Mata Pelajaran Sejarah

This study aimed to identify the impact of using the Kahoot application in measuring student achievement for Level 1 history subjects. The objectives of this study were to develop achievement testing tools using the Kahoot application for History subjects; and identify differences in use of the Kahoot application and written tests in measuring student achievement. The design of this study was quantitatively based on 30 participants from Form 1 students at a school in Malacca. The study applied survey methods using questionnaires and experiments using test sets to see the impact of student achievement on History subjects using two different testing tools. The results showed that testing tools using the Kahoot app gave students positive results and the achievement for students using the Kahoot app was higher than using conventional testing tools.
An Intelligent Context Aware Recommender System for Real-Estate

Finding products and items in large online space that meet user needs is difficult. Time spent searching before finding a relevant item can be a significant time sink for users. As with other economic branches, growing Internet usage also changed user behavior in the real-estate market. Advancements in virtual reality offer virtual tours and interactive map and floor plans which make an online rental websites very popular among users. With the abundance of information, recommender systems become more important than ever to give the user relevant property suggestions and reduce search time. A sophisticated recommender in this domain can help reduce the need of a real-estate agent. Session-based user behavior and lack of user profiles leads to the use of traditional recommendation methods. In this research, we propose an approach for real-estate recommendation based on Gated Orthogonal Recurrent Unit (GORU) and Weighted Cosine Similarity. GORU captures the user search context and weighted cosine similarity improves the rank of pertinent property. We have used the data of an online public real estate web portal (AARZ.PK). The data represents the original behavior of the user on an online portal. We have used Recall, User coverage and Mean Reciprocal Rank (MRR) metrics for the evaluation of our system against other state-of-the-art techniques. The proposed solution outperforms various baselines and state-of-the-art RNN based solutions.