Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time facial expression recognition using smoothed deep neural network ensemble

Facial emotion recognition (FER) has been extensively researched over the past two decades due to its direct impact in the computer vision and affective robotics fields. However, the available datasets to train these models include often miss-labelled data due to the labellers bias that drives the model to learn incorrect features. In this paper, a facial emotion recognition system is proposed, addressing automatic face detection and facial expression recognition separately, the latter is performed by a set of only four deep convolutional neural network respect to an ensembling approach, while a label smoothing technique is applied to deal with the miss-labelled training data. The proposed system takes only 13.48 ms using a dedicated graphics processing unit (GPU) and 141.97 ms using a CPU to recognize facial emotions and reaches the current state-of-the-art performances regarding the challenging databases, FER2013, SFEW 2.0, and ExpW, giving recognition accuracies of 72.72%, 51.97%, and 71.82% respectively.
The Implications of Big Data and Social Media Analytics on Industry 4.0

— The world is now in the midst of a massive digital transformation. This transition was so great that the term Industry 4.0 rose to represent the fourth revolution that had occurred in various fields. The fourth industrial revolution continued what had begun in the third industrial revolution with automation and intelligently upgraded computerization through autonomous systems driven by data and machine learning. Because industry 4.0 is very consumer-centric, businesses are forced to innovate to satisfy and make consumers happy about their products. To did so, businesses turned to Big data and social media analytics. These new technologies are used by many companies from various industries such as manufacturing, logistics, and even by political figures to win elections. This article was written to explain the importance of using big data technology and social media analytics in the current industrial era 4.0.
SEMANTIC MEASURES IN DESIGN CONVERSATIONS AS PREDICTORS OF CREATIVE OUTCOMES IN DESIGN EDUCATION

The analysis of conversations maintained during the design activity can help to gain a better insight into design thinking and its relation to creativity. A semantic analysis approach was employed to inspect the content of communications and information exchange between students and instructors. The goal was to explore design conversations in terms of Abstraction, Polysemy, Information Content and Semantic Similarity measures, and analyse their relation to the creativity of final design outcomes. These were assessed according to their Originality, Usability, Feasibility, Overall Value and Overall Creativity. To this end, design conversations from the 10th Design Thinking Research Symposium (DTRS10) dataset were used. Main results show a significant relationship between Information Content and Originality and Overall Creativity. For instructors, Semantic measures were mainly related to Feasibility, whereas for students the focus was set on the Overall Value of the final solutions.
Induced immune responses in Formica fusca (Hymenoptera: Formicidae)

Parental immune experience can enhance offspring defence mechanisms towards prevalent pathogens in the surrounding environment. This process of inherited resistance from one generation to another is known as trans-generational immune priming (TGIP) in invertebrates. In sedentary and dense insect societies, such as ant colonies, TGIP can influence colony survival and fitness upon pathogen outbreaks. However, TGIP appears to depend on species and environmental stressors and therefore can vary in intensity, as well as in the molecular mechanisms leading to resistance. Here, we stimulated the immune system of queens of the ant Formica fusca (Linnaeus, 1758) by wounding or injecting dead conidia of the entomopathogenic fungus Beauveria bassiana (Bals.-Criv.) Vuill. (1912). The offspring were subsequently infected with B. bassiana , and the effect of this priming on survival was evaluated. Furthermore, we investigated whether immune challenge of the mother queen induces changes in the expression of immunity-related genes in queens themselves and their brood. We combined this information with measurements of offspring size and number. Larvae produced by untreated queens had a significantly higher mortality after infection with B. bassiana , whereas those produced by immune-primed queens survived no worse than unexposed ones. Adult worker offspring from sham-control mothers showed a protective effect of queen treatment, consistent with transgenerational immune priming. Thus, the effects of queen priming appear to manifest themselves slightly differently in larval and adult offspring. No differences were detected in offspring number or size, but immune gene expression levels showed changes, both in queens and their offspring. Open access, licensed under CC BY 4.0. © 2018 The Author(s). Pages: 53-66 DOI: 10.25849/myrmecol. news_028:053 Volume: 28 Year: 2018 Journal: Myrmecol. New
Unsupervised Spectral Feature Selection With Dynamic Hyper-Graph Learning

Unsupervised spectral feature selection (USFS) methods could output interpretable and discriminative results by embedding a Laplacian regularizer in the framework of sparse feature selection to keep the local similarity of the training samples. To do this, USFS methods usually construct the Laplacian matrix using either a general-graph or a hyper-graph on the original data. Usually, a general-graph could measure the relationship between two samples while a hyper-graph could measure the relationship among no less than two samples. Obviously, the general-graph is a special case of the hyper-graph and the hyper-graph may capture more complex structure of samples than the general graph. However, in previous USFS methods, the construction of the Laplacian matrix is separated from the process of feature selection. Moreover, the original data usually contain noise. Each of them makes difficult to output reliable feature selection models. In this paper, we propose a novel feature selection method by dynamically constructing a hyper-graph based Laplacian matrix in the framework of sparse feature selection. Experimental results on real datasets showed that our proposed method outperformed the state-of-the-art methods in terms of both clustering and segmentation tasks.
EFFICIENT EYE BLINK COMMUNICATION ASSISTANCE FOR PARALYZED PATIENTS

The research study presents a real time method based on some video and image processing algorithms for eye blink detection to voice conversion. The motivation of this study is to help the disabled people who cannot communicate with human. The Haar Cascade Classifier is used for face and eye recognition to gain details about the eyes and facial axes. In comparison, the same classifier is used to assess the relation among the senses like eye and axis of the face to position the eye based on haar like features. This proposes an effective eye detection system which uses the sensed face location. An efficient eye detection method is proposed which uses the position of detected face. Finally, an eye blink detection method is done based on eyelids movement whether it is open or closed and is used for controlling mobile phones. The study have designed a very low-priced device that coverts the eye blinks to voice message with more accuracy compared to existing system. The eye blinks that are detected can be helpful in applications such as health assistance, S.O. S, basic utility. Test results show that OUR PROPOSED SYSTEM FOR A DISTANCE OF 35CM DELIVERS A OVERALL ACCURACY OF 98% AND A DETECTION ACCURACY OF 98%
Association Mapping for Common Bunt Resistance in Wheat Landraces and Cultivars

Common bunt is a seed borne disease of wheat whose importance is likely to increase due to the growing organic seed market, which, in addition to seed phytosanitary measures, relies on genetic resistances towards the disease. Genome wide association studies in wheat have been proven to be a useful tool in the detection of genetic polymorphisms underlying phenotypic trait variation in wheat. Here 248 wheat landraces and cultivars representing 130 years of breeding history were screened for two years in the field for their resistance reactions towards common bunt. The majority of lines exhibited high levels of susceptibility towards common bunt, while 25 accessions had less than 10% infection. Using Diversity Array Technology (DArT) markers for genotyping and correcting for population stratification by using a compressed mixed linear model, we identified two significant marker trait associations (MTA) for common bunt resistance, designated QCbt.cph-2B and QCbt.cph-7A, located on wheat chromosomes 2B and 7A, respectively. This shows that genome wide association studies (GWAS) are applicable in the search for genetic polymorphisms for resistance towards less studied plant diseases such as common bunt in the context of an under representation of resistant lines.
Application of a New Greyscale Descriptor for Recognition of Erythrocytes Extracted from Digital Microscopic Images

In the paper an algorithm for description of greyscale objects extracted from images is applied for recognition of human red blood cells visible on digital microscopic images. This is a part of an approach for automatic (or semi-automatic) diagnosis of selected diseases based on the deformation of erythrocytes. The disease is concluded by means of the recognition of types of red blood cells visible on an digital microscopic image, stained using MGG method and converted into greyscale. The applied algorithm is based on the polar transform of pixels belonging to an object and a specialized method for constituting the resultant description, where derived coordinates are put into matrix, in which the row corresponds to the distance from the centre, and the column—to the angle. This assumption was previously applied only for shape features. The proposed algorithm includes several auxiliary steps, e.g. median and low-pass ﬁltering in order to pre-process the extracted object in greyscale. The algorithm is experimentally evaluated and analysed. It is compared with four other greyscale descriptors, namely: Scale-Invariant Feature Transform, Gabor ﬁlter, Polar-Fourier Greyscale Descriptor, and the approach based on polar transform and projections.
International Public Administrations on Twitter: A Comparison of Digital Authority in Global Climate Policy

Abstract International organizations and their secretariats, called international public administrations (IPAs), have been found to hold considerable authority in world politics. This study conceptualizes and measures IPA authority in the digital sphere. It proposes the concept of digital authority to measure the authority of actors in online social networks (OSN), such as Twitter. Applying exponential random graph models (ERGMs) based on Twitter data during climate change negotiations the article compares the authority of IPAs to that of other actors. The findings show that IPAs are attributed as much authority as state actors in global climate communication networks on Twitter.
A New Modulation Technique to Improve the Power Loss Division Performance of the Multilevel Inverters

Multilevel inverters (MLIs) have offered significant contributions toward the implementation of energy conversion units used in industry and renewable energy applications. However, the switching devices in MLIs are associated with power loss division problems. The pulsewidth modulation (PWM) techniques greatly influence the peak, average, and ripple values of the device junction temperature. Thermal tension in the devices may be raised due to increased power loss caused by additional switching or uneven distribution of losses due to unbalanced gate pulses. Higher thermal stress accelerates the life degradation process of the power devices, and, eventually, leads to permanent damage of the MLI. Therefore, a new loss balancing two-pole clamped PWM technique for a single-phase MLI is proposed in this article. In the proposed PWM technique, in each half line cycle, symmetrical and equal duration of nonswitching zone is placed on the two reference signals alternately. Due to symmetrical and equal duration of clamping action, the unbalanced gate pulses become balanced after each half line cycle. The proposed technique extends the reliability of the power devices by improving the loss division performance and the junction temperature characteristics. Moreover, the reduction of neutral point potential swing reduces the output total harmonic distortion. The improved performance of the proposed technique is validated by simulation in MATLAB/Simulink and experimental results from downscale laboratory prototype test platform.
Modeling and Control of a Two Wheeled Self-Balancing Robot: a didactic platform for control engineering education.

This paper describes the modeling, instrumentation and control of a Two Wheeled Automatic Balancing Robot (TWABR). This mechatronic system has two independently driven wheels to balance in the gravity center above the axis of the wheels ́ rotation. Its dynamic behavior has also served to illustrate fundamental concepts of stability, nonlinear dynamics, and modern control theory. The TWABR was designed using the ESP32 microcontroller as a digital control unit and the MPU6050 Inertial Measurement Unit as the main sensor. The dynamic model of the TWABR was analyzed through its representation in nonlinear differential equations and its linearized representation in the state space. With the linearized mathematical model around the equilibrium point, a classic PID controller and an optimal LQR controller were designed and simulated. The control objective was to balance the TWABR in the vertical equilibrium position, even when it is subjected to disturbances. The two control algorithms were simulated in the Matlab / Simulink software platforms and implemented digitally on the physical TWABR system. As a result, the experimental comparison of the performance of the implemented controllers was performed, where its stabilization, control robustness and adequate dynamic response at the equilibrium point of the TWABR were evaluated. Keywords—States Space Model, Automatic Balancing Robot, Digital Control, PID and LQR Controllers.
Astronomical calibration of Oligocene--Miocene time

Lithological cyclicity was observed aboard the JOIDES RESOLUTION in sediment sequences recovered from the Ceara Rise during ODP Leg 154. Shipboard work led to the conclusion that the Oligocene was probably characterized by ca. 41 ka cycles. Weedon and others were able to confirm this, and created a provisional time–scale for the Oligocene by assuming that the cyclicity is a response to orbital obliquity variation, and by using spectral analysis to estimate the mean wavelength and hence the sedimentation rate of successive intervals of core. We have extended this work by intercorrelating almost all the 9.5 m sediment cores from each of the four sites that recovered Oligocene sediment. We have successfully correlated all the material covering a time–interval of ca. 10 Ma from 18 Ma to 28 Ma, as well as most of the sediment from the 14 to 18 Ma and 28 to 34 Ma intervals. Although variability is dominated by the 41 ka cycle there is sufficient variability at the precession period (amplitude–modulated by eccentricity) to permit an absolute placement of this section with reference to the calculated orbital history. Further work is needed to establish precisely the implications of this calibration for the geological time–scale but it appears that the true ages of events close to the Oligocene–Miocene boundary are ca. 0.9 Ma younger than they appear on recently published time–scales. The sedimentary record preserves information concerning the amplitude modulation of the obliquity signal that is of astronomical as well as geological significance.
Deep Learning-Enabled Sparse Industrial Crowdsensing and Prediction

Mobile Crowdsensing (MCS) is a powerful sensing paradigm, which provides sufficient social data for cognitive analytics in industrial sensing, and industrial manufacturing. Considering the sensing costs, sparse MCS, as a variant, only senses the data in a few subareas, and then infers the data of unsensed subareas by the spatio-temporal relationship of the sensed data. Existing works usually assume that the sensed data are linearly spatiotemporal dependent, which cannot work well in real-world nonlinear systems, and thus, result in low data inference accuracy. Moreover, in many cases, users not only require inferring the current data, but also have an interest in predicting the near future, which can provide more information for users’ decision making. Facing these problems, we propose a deep learning-enabled industrial sensing, and prediction scheme based on sparse MCS, which consists of two parts: matrix completion and future prediction. Our goal is to achieve high-precision prediction of future moments under the hypothesis of sparse historical data. To make full use of the sparse data for prediction, we first propose a deep matrix factorization method, which can retain the nonlinear temporal-spatial relationship, and perform high-precision matrix completion. In order to predict the subareas’ data in several future sensing cycles, we further propose a nonlinear autoregressive neural network, and a stacked denoising autoencoder to obtain the temporal–spatial correlation between the data from different cycles or subareas. According to the results gained by experiments on four real-world industrial sensing datasets consisting of six typical tasks, it can be seen that the method in this article improves the accuracy of prediction using sparse data.
Technological Schemes at the Transverse Mining System of the Open Pit Mine’s Bottom Layer

At the quarry fields with inclined and steep occurrence of complex structural coal seams formations of Kuzbass, it is preferable to use transverse mining systems. In this case, transport costs are significantly reduced and alienation of land areas for external dumps is reduced too. It is also possible to apply an effective transportless technology of stripping operations. Absence at this technology of expensive and ecologically dangerous quarry dump trucks allows extracting coal with smaller expenses, makes it more competitive in the domestic and foreign coal market. However, for the design of such technology in the conditions of transverse direction of the mining front, at present, theoretical studies of issues related to the justification of its parameters and the choice of mining equipment are insufficient. A wide range of coal bed formation conditions, a wide range of standard sizes of draglines used in the transportless technology, as well as various values of the depth of mining predetermine the multi-variant solution of the above problems. In this case, the formation of possible variants of mining operations with the use of certain models of excavators for their subsequent evaluation by any criterion is in itself a rather complicated task.
Blockchain-Enabled Certificate-Based Authentication for Vehicle Accident Detection and Notification in Intelligent Transportation Systems

As the communications among the vehicles, the Road-Side Units <inline-formula> <tex-math notation="LaTeX">$(\textit {RSU})$ </tex-math></inline-formula> and the Edge Servers <inline-formula> <tex-math notation="LaTeX">$(\textit {ES})$ </tex-math></inline-formula> take place via wireless communication and the Internet, an adversary may take the opportunity to tamper with the data communicated among various entities in an Internet of Vehicles (IoV) environment. Therefore, it demands secure communication among the involved entities in an IoV-based Intelligent Transportation System (ITS) deployment. In this work, we design a new blockchain-enabled certificate-based authentication scheme for vehicle accident detection and notification in ITS, called BCAS-VADN. In BCAS-VADN, through the authentication process, each vehicle can securely notify accident related transactions to its nearby Cluster Head <inline-formula> <tex-math notation="LaTeX">$(\textit {CH})$ </tex-math></inline-formula>, if an accident is detected on roads either by its own or neighbor vehicle(s). The <inline-formula> <tex-math notation="LaTeX">$\textit {CH}$ </tex-math></inline-formula> then securely sends the transactions received from the vehicles to its <inline-formula> <tex-math notation="LaTeX">$\textit {RSU}$ </tex-math></inline-formula> and subsequently, these transactions are also received securely by the <inline-formula> <tex-math notation="LaTeX">$\textit {ES}\text{s}$ </tex-math></inline-formula>. The <inline-formula> <tex-math notation="LaTeX">$\textit {ES}$ </tex-math></inline-formula> is responsible for preparing partial block containing transactions and Merkle tree root, and a digital signature on those information, and then forwarding to its associated Cloud Server <inline-formula> <tex-math notation="LaTeX">$(\textit {CS})$ </tex-math></inline-formula> in the Blockchain Center <inline-formula> <tex-math notation="LaTeX">$(\textit {BC})$ </tex-math></inline-formula> for complete block creation, verification and addition of the block using the designed consensus process. Due to blockchain technology usage, it is shown that BCAS-VADN is not only secure against various potential attacks, but also maintains transparency, immutability and decentralization of the information. Furthermore, a comprehensive comparative analysis reveals that BCAS-VADN achieves better security and more functionality attributes, and has low communication and computational overheads as compared to other competitive authentication schemes in IoV. In addition, the practical demonstration using the blockchain technology has been also provided.
Listening to #2A: Applying a Qualitative Method to Twitter Dialogue

In this article, we present a project that explored the application of an established qualitative methodology to a novel source of data: microblog postings on the social media platform Twitter, also known as tweets. In particular, we adapted Consensual Qualitative Research (CQR; Hill, Thompson, & Williams, 1997) for use in this analysis. The coinciding aim of the project was to study the cultural impasses that seemed to characterize U.S. society surrounding the 2016 presidential election. Publicly available tweets bearing the hashtag #2A were selected for examination; this hashtag indicated the user’s intention to direct the posting to the attention of Twitter users in the context of the Second Amendment, which refers to citizens’ right to bear arms. The article describes the process by which CQR was modified for this use, profiles the exploratory findings, and present suggestions for subsequent similar research undertakings.