Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation

Emotion Recognition in Conversation (ERC) aims to recognize the emotion for each utterance in a conversation automatically. Due to the difficulty of collecting and labeling, this task lacks the dataset corpora available on a large scale. This increases the difficulty of finishing the supervised training required by large-scale neural networks. Introducing the large-scale generative conversational dataset can assist with modeling dialogue. However, the spatial distribution of feature vectors in the source and target domains is inconsistent after introducing the external dataset. To alleviate the problem, we propose a Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation (DAN-CDERC) model, consisting of domain adversarial and emotion recognition models. The domain adversarial model consists of the encoders, a generator and a domain discriminator. First, the encoders and generator learn contextual features from a large-scale source dataset. The discriminator performs domain adaptation by discriminating the domain to make the feature space of the source and target domain consistent, so as to obtain domain invariant features. Then DAN-CDERC transfers the learned domain invariant dialogue context knowledge from the domain adversarial model to the emotion recognition model to assist in modeling the dialogue context. Due to the use of a domain adversarial network, DAN-CDERC obtains dialogue-level contextual information that is domain invariant, thereby reducing the negative impact of inconsistency in domain space. Empirical studies illustrate that the proposed model outperforms the baseline models on three benchmark emotion recognition datasets.
RAMAN and Fluorimetric Scattering Lidar Facilitated to Detect Damaged Potatoes by Determination of Spectra

The purpose of this study is to substantiate the concept of express assessment of potato tubers for mechanized harvesting using fluorimetric and RAMAN lidar, as well as to conduct a quantitative and structural assessment of damage to potato varieties and hybrids, using a device that simulates the process of operation of the combine separator, depending on the type of machine and harvesting conditions. In addition, calibration of the lidar was undertaken to assess the magnitude of the physical impact of the drum to assess the damage. A structural–functional diagram of a device for assessing the suitability of varieties and hybrids for mechanized harvesting has been developed. The methodology and results of this study are presented to determine the assessment of damage to potato tubers by a RAMAN lidar when fixing their force impact of the separating surface of the developed device on a potato tuber. The device developed in accordance with the proposed conceptual scheme, additionally calibrated taking into account modern potato harvesters and tested on a RAMAN lidar, will allow targeted selection of varieties suitable for mechanized harvesting, as well as assessing the degree of resistance of potato varieties to mechanized harvesting at early stages of selection and seed works.
Organization of the Colicin Ib Gene PROMOTER STRUCTURE AND IMMUNITY

The colicin Ib gene of the large low-copy-number plasmid Col Ib-P9 has been cloned into the plasmid vector pBR322. Observations of growth characteris- tics of strains carrying the cloned gene and analysis of insertion and deletion derivatives of the recombinant plasmid have led to the conclusion that colicin Ib production can occur without the colicin Ib immunity sys- tem. The direction of transcription for the colicin Ib gene was determined by use of an expression vector and subsequently confirmed by DNA-sequence analysis of the colicin Ib promoter. The physical maps of the cloned colicin Ia and colicin Ib genes exhibit a great deal of similarity which has enabled the construction of hybrid genes consisting of the N terminus of one colicin gene being linked to the C terminus of the other. From an analysis of the chimeric colicins and the lo- cations of the fusions, we conclude that the information necessary for immunity recognition by colicin Ia and colicin Ib resides in the C-terminal half of the colicin proteins.
Retrograde Approach to Maxillary Nerve Block: An Alternative in Orofacial Surgeries in Horses

Simple Summary Maxillary nerve (MN) desensitization can improve quality and safety of orofacial surgeries in horses, especially when a standing procedure is elected. The purpose of this retrospective study is to report results from 15 horses undergoing orofacial surgery which received an MN block with the retrograde approach within the infraorbital canal. The same technique was used in horses scheduled for orofacial surgeries and grouped as requiring general anesthesia or standing analgo-sedation. From the retrieved anesthetic records, regardless of the group, clinical and physiological parameters continuously monitored during surgery were stable, and none of the horses showed complications during or after the block. These results confirm the feasibility of this loco-regional anesthetic technique as a valid approach to the MN without risk of damaging periorbital structures, which is reported with traditional techniques. Evident landmarks for this approach and limited chances to injure vital structures enclosed within the infraorbital canal make the operator experience less relevant than the ability required to block the MN according to approaches, which currently aim at the pterygopalatine fossa. This substantiates the retrograde approach to block the MN is safe and easy to perform in horses undergoing orofacial surgeries at regions where this nerve provides sensory innervation. Abstract The aim of this study was to retrospectively report outcomes resulting from the approach to the maxillary nerve block (MNB) through the infraorbital canal, in terms of needles selection, drawbacks or side effects during or after block execution, and analgesic efficacy leading to clinical and cardiovascular stability during surgery. Anesthetic records of 15 horses undergoing orofacial surgery in standing analgo-sedation (STA, n = 6) and in general anesthesia (GEN, n = 9) were retrieved and analyzed. Horses in group STA required surgery for dental extraction, nasal polyp resection and maxillary/frontal sinusitis. Horses in group GEN underwent surgery for fronto-maxillary, nasal and dental diseases. Size 19 and 20 G Tuohy needles were used in adult horses weighing 350–600 kg, while size 21 and 22 G were used in younger horses or ponies. None of the horses in both groups showed complications related to the block and physiological parameters were stable and within normal ranges during surgery; overall, an adequate anesthetic/sedation depth was achieved. Our results confirm the in vivo applicability of the MNB approached within the infraorbital canal, which had been described only on cadaveric specimens. The retrograde technique resulted in a valid and easy approach to the maxillary nerve that avoids damage to periorbital structures and side effects reported with traditional techniques.
An Active Disturbance Rejection Sliding Mode Contour Tracking Control for Biaxial Moving Platform

A sliding mode control for active disturbance rejection was utilized for uniaxial tracking control, and a cross-coupled contour error compensation controller based on nonlinear position domain (PD) was devised for enhanced contour machining precision and robustness of biaxial moving platform. For single-axis tracking control, the total disturbance of the system was initially considered to be the external disturbance plus internal unmodeled dynamics, and the overall disturbance was estimated and compensated in real time using a linear extended state observer. A sliding mode controller was built on this foundation to obtain satisfactory uniaxial control performance. Secondly, based on the estimated value of each axis component of contour error, a nonlinear PD contour error compensation controller was designed to coordinate the tracking motion between axes and further reduced the contour error. The proposed work is compared against state-of-the-art works from literature for various metrics such as tracking error curves, average comparison of tracking errors, and cross-coupled controller parameters. The simulation results showed that the proposed method can effectively improve the contour processing accuracy of the system.
Several Issues of Generalized Physical and Mathematical Modeling of Micro- and Nano-Electromechanical Systems (MEMS and NEMS)

In the article the modern state of the electromechanical science is briefly reviewed, and certain prospective application fields of the newest achievements in nanoscience and nanotechnology are considered in those areas of human vital activities (from medicine up to space exploration), the scientific-technical progress in which is based on a comprehensive use of the electromechanical energy converters. The research considers some of the key issues (topical points, nodal points) of the generalized (combined) physical and mathematical modeling of microminiature and nanoelectomechanical systems (MEMS and NEMS). A new generalized approach is proposed to the research of the dynamic and energetic performance of MEMS and NEMS, as complex dynamic systems with binary conjugated subsystems. The presented theoretical principles and models are used as the basis for research of the electrophysical characteristics of the biological nanostructures.
Minimizing thermal damage of aerospace components using coolant nozzle and coolant system optimization

Research to optimize the application of coolant in the creep feed grinding of aerospace components was conducted at the Center for Grinding Research and Development during the past year. During this research, work was performed in the areas of coolant jet nozzle and coolant system design to identify optimum jet nozzle designs, nozzle positioning and coolant system configurations. The knowledge gained from initial screening tests and grinding trials of flat surfaces was applied to final grinding trials on actual blade and vane (profiled) production components. Final grinding test results of four specific production operations showed that at least a 27% improvement in wheel life could be realized, relative to the levels previously established in production, by optimizing grinding fluid application. In addition, a set of guidelines for optimized coolant nozzle and coolant system design and manufacture have been developed from the results of this research, and are applicable to other types of grinding or machining as well.
Vision based estimation, localization, and mapping for autonomous vehicles

In this dissertation, we focus on developing simultaneous localization and mapping (SLAM) algorithms with a robot-centric estimation framework primarily using monocular vision sensors. A primary contribution of this work is to use a robot-centric mapping framework concurrently with a world-centric localization method. We exploit the differential equation of motion of the normalized pixel coordinates of each point feature in the robot body frame. Another contribution of our work is to exploit a multiple-view geometry formulation with initial and current view projection of point features. We extract the features from objects surrounding the river and their reflections. The correspondences of the features are used along with the attitude and altitude information of the robot. We demonstrate that the observability of the estimation system is improved by applying our robot-centric mapping framework and multiple-view measurements. Using the robot-centric mapping framework and multiple-view measurements including reflection of features, we present a vision based localization and mapping algorithm that we developed for an unmanned aerial vehicle (UAV) flying in a riverine environment. Our algorithm estimates the 3D positions of point features along a river and the pose of the UAV. Our UAV is equipped with a lightweight monocular camera, an inertial measurement unit (IMU), a magnetometer, an altimeter, and an onboard computer. To our knowledge, we report the first result that exploits the reflections of features in a riverine environment for localization and mapping. We also present an omnidirectional vision based localization and mapping system for a lawn mowing robot. Our algorithm can detect whether the robotic mower is contained in a permitted area. Our robotic mower is modified with an omnidirectional camera, an IMU, a magnetometer, and a vehicle speed sensor. Here, we also exploit the robot-centric mapping framework. The estimator in our system generates a 3D point based map with landmarks. Concurrently, the estimator defines a boundary of the mowing area by using the estimated trajectory of the mower. The estimated boundary and the landmark map are provided for the estimation of the mowing location and for the containment detection. First, we derive a nonlinear observer with contraction analysis and pseudo-measurements of the depth of each landmark to prevent the map estimator from diverging. Of particular interest for this work is ensuring that the estimator for
Lung nodule classification based on deep learning networks and handcraft segmentation

This study proposes a Hybrid CAD system, where the first stage consists of the handcraft segmentation, following a CNN based on the ResNet-34 architecture. In the segmentation stage, the rib cage (thorax region) is extracted using the K-means algorithm. The extraction of the nodules is performed in two steps, those attached to the pleura are found via a hysteresis threshold on the rib cage. The circumscribed and vascular nodules are extracted using morphological operations. The resulting segmentation masks are applied to the test images, decreasing the number of false positives. Finally, the resulting image is splitted of in patches to be classified by the ResNet-34 trained from scratch. Designed CAD system has been implemented on Google Collab platform and a standalone computer with Nvidia RTX 3090. The experiments with different CAD systems were performed on SPIE and LIDC-IDRI datasets demonstrating better performance of designed technique with reduction of false-positive objects.
Focal field engineered infrared-sensitive third-order sum frequency generation microscopy

In this work we present experimental demonstration of focal-field engineering in infrared-sensitive third-order sum frequency generation (TSFG) microscopy by utilizing beam-shaping technique. Two photons of the input mid-infrared (MIR) beam at 3000 nm are upconverted to 615 nm in the presence of a single photon at 1040 nm through the TSFG process. The focal-field engineering scheme studied here improves optical resolution and contrast of the TSFG imaging. We observe best improvement of ~43 % in the central-lobe full-width half diameter with ~35% side-lobe strength of that of the central-lobe with the use of optimum phase-mask using isolated amorphous silicon (a-Si) nano disks as the sample. We compare the contrast enhancement between the experiments and simulations as a function of varying grating pitch and find good overall agreement between the two. In addition to annular phase masks, we also demonstrate edge contrast enhancement by imaging gratings with higher-order Hermite-Gaussian beams profile generated using horizontally partitioned 0-𝜋 phase profile.
Performance Analysis of Mobile Cloud Computing With Bursty Demand: A Tandem Queue Model

Resource-constrained end devices can offload computation to backend clouds. The stochastic wireless channel that an end device is connected to can introduce bursty computation demand to the cloud. Specifically, under good channel conditions, a device can transmit more data to the cloud, which consequently yields higher instantaneous computation demand. Conversely, poor channel conditions can result in lower instantaneous demand. The performance indicator for such a mobile cloud computing system is the average of the response time, which is the time span from the arrival of the computation demand at the backend cloud instance to the completion of its execution. The question we target in this paper is how resources should be provisioned for the backend cloud instance to address this bursty computation demand and guarantee a desired quality-of-service (QoS), namely, a user-specified average response time. To answer this question, we model the mobile cloud computing system as two tandem queues. We analyze this queueing network using the fluid flow analysis framework, and derive the analytical relationship between the required resource capacity at the backend cloud instance and the desired QoS, given the workload generation process at the end device and the wireless channel conditions. Having obtained the required resource capacity for a desired QoS, we then determine whether it is economical to provision this resource capacity by subscribing to the traditional static instance or the recently introduced burstable instance offered by public cloud providers. Finally, trace-driven simulations validate our theoretical results.
Factors Affecting Farmers’ Uses of Information Sources in Vietnam

Every information source has advantages and disadvantages for delivering certain types of information. However information sources have generally been recognized as tools that improve the efficiency and effectiveness of agriculture as collecting and distributing agricultural information is difficult, time-consuming, and expensive. The purpose of this study is to identify factors that explain the variation among farmers in their use of information sources based on household (farm)-level data. The results show that household income, farm size, household size, educational attainment, and off-farm jobs are the most important factors influencing the use of media and personal information sources. The implications of these results are introducing and encouraging more off-farm jobs for farmers. As such, the provincial government and extension agents are recommended to work more effectively to enhance and help farmers diversifying crops and applying technologies. It is also important to improve the quantity and quality of extension services. The results also suggest that adjusting policies for reducing the gap among districts are needed, and farmers’ education should focus on training for agricultural knowledge, social activities, governmental policies, as well as, using informational sources, such as the Internet, computers, etc.
A non-invasive approach for total cholesterol level prediction using machine learning

Artificial intelligence techniques have been increasingly applied in healthcare to help in many areas, from assisting clinical diagnoses to preventing diseases. In this paper, a machine learning approach to predict cholesterol levels using non-invasive and easy-to-collect data is presented. Specifically, it uses clinical and anthropometric data gathered by nutritionists during weight loss intervention (dieting) periods. The prediction power analysis of different patient variables is aimed at improving both non-invasive diagnosis quality and screening of associated diseases. Moreover, a clustering analysis has been carried out to identify different groupings of patients that might share some characteristics that have so far remained inconspicuous but might contain a valuable diagnosis or prognosis information for clinical experts.The experiments show a mean absolute percentage error rate (MAPE) of 4.39% in cholesterol estimation via regression, as well as clustering of patients within four profiles in which variable values share commonalities among cluster members.
Constructing twitter corpus of Iraqi Arabic Dialect (CIAD) for sentiment analysis

The number of Twitter users in Iraq has increased significantly in recent years. Major events, the political situation in the country, had a significant impact on the content of Twitter and affected the tweets of Iraqi users. Creating an Iraqi Arabic Dialect corpus is crucial for sentiment analysis to study such behaviors. Since no such corpus existed, this paper introduces the Corpus of Iraqi Arabic Dialect (CIAD). The corpus has been collected, annotated and made publicly accessible to other researchers for further investigation. Furthermore, the created corpus has been validated using eight different combinations of four feature-selections approaches and two versions of Support Vector Machine (SVM) algorithm. Various performance measures were calculated. The obtained accuracy, 78 %, indicates a promising potential application.
A method to measure data complexity of a complicated medical data set

In this article, we consider data complexity in the context of calcium transient signal data collected from induced pluripotent stem cell‐derived cardiomyocytes. We present a novel way to measure data complexity based on the nearest neighbour searching method. Data complexity here is seen as overlapping and mixed data classes in addition to a relatively great number of data cases. Complexity affects classification results, which were run with nearest neighbour searching, feedforward artificial neural networks and random forests for seven genetic cardiological disease classes and healthy controls. The data are obtained from individuals carrying mutations for genetic cardiac diseases with induced pluripotent stem cell (iPSC) technology and the diseases include hypertrophic cardiomyopathy with two different founder gene mutations, dilated cardiomyopathy, long QT syndrome type 1 and 2, Brugada syndrome, a severe genetic ventricular arrhythmia (CPVT) and healthy controls. The data are from calcium transients from spontaneously beating iPSC‐derived cardiomyocytes cultured in a biotechnology laboratory. When the genotype of the iPSC‐derived cardiomyocytes is the same as the donor of the tissue sample and based on the characteristics of the calcium transients, it was possible to classify the seven diseases and healthy controls with machine learning. Peak data first detected before actual pre‐processing from calcium transient signals corresponded to beats (repeating excitation–contraction coupling) of induced stem cell‐derived cardiomyocytes and formed the basis of classification. During pre‐processing of the calcium transient signals, we found that such techniques among others as even strong outlier cleaning or class size balancing by generating artificial cases improved only slightly or not at all classification accuracies. Therefore, the current data set was sufficiently complicated for our data complexity study. Random forests produced the best classification accuracies, 68% for all eight classes.