Miracle-dz/UniFAR-FTUs huggingface dataset:
Productive Performance and Cost-Effectiveness of Using Additional Vitamins in Commercial Broiler Diets

One-day old Cobb 500 broiler chicks (252) were divided into three treatment groups each of 84 birds (four replicates each of 21 birds). During an experimental period of 30 days, one group was administered multivitamins in drinking water continuously, the other group only during stress conditions and a control was maintained without additional vitamin supplement. Performance of birds and profitability were improved significantly (P<0.01) for the birds in performances of birds reared on multivitamins. It was concluded that continuous and intermittent (during stress conditions) supply of multivitamins improved the performance and profitability of commercial broilers.
Hybrid Motion Reproduction Using Master/Slave Type Motion-Copying System

This paper proposes a hybrid motion reproduction structure using a master-slave structure manipulator based on a motion-copying system. Data-driven control will be an important technique in the near future. Learning from Demonstration (LfD) using a robot manipulator is already used in manufacturing. Unlike other motion observation methods based on visual or trajectory responses, a motion-copying system can obtain not only trajectory response but also force response during contact motion, which is necessary to realize contact tasks. A motion-copying system has two phases, the motion saving phase and motion reproduction phase. In the motion reproduction phase, the motion stored in the motion saving phase obtained from the bilateral control can be used to reproduce speciﬁc motion. Existing control methods for a motion-copying system have only one system in the reproduction phase, a slave system. It is necessary to consider the case where there are two systems in the phase to widen the application of a motion-copying system. The additional system can provide ﬂexibility to the reproduced motion. In this case, data, human, and a robot are involved in reproducing the motion. In this paper, the control structure is proposed when there are two systems in the motion reproduction phase. Because there are two systems, it is necessary to design the controllers for each system. The proposed method uses a coordinate transformation technique to design two motions in each system separately. The paper also introduces a novel index called the reproduction ratio. It determines the motion to be reproduced in the motion reproduction phase. The ratio is used as an element of a coordinate transformation matrix. The experimental results show the validity of the proposed method.
Quick and Plenty: Achieving Low Delay and High Rate in 802.11ac Edge Networks

We consider transport layer approaches for achieving high rate, low delay communication over edge paths where the bottleneck is an 802.11ac WLAN. We first show that by regulating send rate so as to maintain a target aggregation level it is possible to realise high rate, low delay communication over 802.11ac WLANs. We then address two important practical issues arising in production networks, namely that (i) many client devices are non-rooted mobile handsets/tablets and (ii) the bottleneck may lie in the backhaul rather than the WLAN, or indeed vary between the two over time. We show that both these issues can be resolved by use of simple and robust machine learning techniques. We present a prototype transport layer implementation of our low delay rate allocation approach and use this to evaluate performance under real radio conditions.
Question Answering Systems Approaches and Challenges

Question answering (QA) systems permit the user to ask a question using natural language, and the system provides a concise and correct answer. QA systems can be implemented for different types of datasets, structured or unstructured. In this paper, some of the recent studies will be reviewed and the limitations will be discussed. Consequently, the current issues are analyzed with the proposed solutions.
Planar Serial type Medical Robot in Minimally Invasive Surgery

: Minimally invasive surgery refers to an array of techniques that aim to minimize the incisions made in the body during a surgical operation. There has been a growing interest in the field owing to numerous benefits like faster recovery times of patient, lower blood loss, less painful post-surgical period etc. We present a framework incorporating an n-link robotic chain with an embedded sensor at the end effector which provides feedback to the surgeon and facilitates correction of digressions from intended path. A numerical validation has been presented which takes the piezometric signal from the end effector and provides real-time corrective signals to minimize tremors and digressions of end effector.
Predicting News Source Credibility

Assessing the credibility of a source of information is important in combating with misin-formation. In this work we tackle the source credibility assessment as regression task. For this purpose we release a dataset containing around 700 news sources along with detailed credibility and transparency scores. These scores are manually assigned to every news source. We merge these scores to have ﬁnal credibility score for every news source. The merged scores are then used to train prediction models. Our results show highly satisfactory performances in predicting the merged credibility scores. Along with the dataset we also plan to release our models to allow the use for a wider community.
Semi-Supervised Multichannel Speech Enhancement With a Deep Speech Prior

This paper describes a semi-supervised multichannel speech enhancement method that uses clean speech data for prior training. Although multichannel nonnegative matrix factorization (MNMF) and its constrained variant called independent low-rank matrix analysis (ILRMA) have successfully been used for unsupervised speech enhancement, the low-rank assumption on the power spectral densities (PSDs) of all sources (speech and noise) does not hold in reality. To solve this problem, we replace a low-rank speech model with a deep generative speech model, i.e., formulate a probabilistic model of noisy speech by integrating a deep speech model, a low-rank noise model, and a full-rank or rank-1 model of spatial characteristics of speech and noise. The deep speech model is trained from clean speech data in an unsupervised auto-encoding variational Bayesian manner. Given multichannel noisy speech spectra, the full-rank or rank-1 spatial covariance matrices and PSDs of speech and noise are estimated in an unsupervised maximum-likelihood manner. Experimental results showed that the full-rank version of the proposed method was significantly better than MNMF, ILRMA, and the rank-1 version. We confirmed that the initialization-sensitivity and local-optimum problems of MNMF with many spatial parameters can be solved by incorporating the precise speech model.
Hyaline thrombosis of the terminal arterioles and capillaries : a hitherto undescribed disease

For several years now, Answering T.T. P. Foundation held an International TTP Day in September to raise awareness of this rare disease. Money is collected for research, but attention is also drawn to the stories of those affected or their family members, combined with training events and the provision of information material. The Answering T.T. P. Foundation was set up in 2009 by Sydney Kodatsky, who had TTP herself. (https://www.answeringttp.org/patient-stories).
Hybrid Satellite-Terrestrial Relay Networks With Adaptive Transmission

Hybrid satellite-terrestrial relay network (HSTRN) has been viewed as a flexible and promising solution to be developed with heterogeneous devices and integrated infrastructures for improved coverage and reduced latency. Taking the requirements of overall spectral and power efficiency into account, we consider different adaptive transmission schemes for the hybrid architecture oriented to potential applications in practical scenarios, and obtain the analytical expressions for the achievable channel capacity with respect to different schemes, i.e., optimal power and rate adaptation, and truncated channel inversion and fixed rate. Numerical simulations confirm the validity of the derived theoretical results, and provide useful guidance for emerging applications in HSTRN that are under various fixed/adaptive transmit power or rate requirements.
Few-Shot Learning under Domain Shift using Adversarial Domain Adaptation

: Few-shot learning aims to tackle the limitations, of classical techniques, of needing large data sets for learning an effective model. Generally, in few shot learning we have a meta-learning stage (learning to learn) which shall be followed by the few examples to learn upon. Considered as a hallmark of human intelligence, the community has recently witnessed several contributions on this topic, in particular through meta-learning, where a model learns how to learn an effective model for few-shot learning. The main idea is to acquire prior knowledge from a set of training tasks, which is then used to perform (few-shot) test tasks. Most work assumes that both training and test tasks are drawn from the same distribution, and a large amount of labelled data is available in the training tasks. This is a very strong assumption which restricts the usage of meta-learning strategies in the real world where ample training tasks following the same distribution as test tasks may not be available. In this paper we shall see a possible approach for the above problem. In this approach, we use Model Agnostic MetaLearning by Finn et al. to address the problem of few shot learning and an adversarial approach to tackle to the problem of domain shift in the target dataset.
Wearable Sensors for Evaluating Driver Drowsiness and High Stress

High levels of stress, drowsiness and lack of concentration, are some of the main factors that affect the drivers, which can lead to traffic congestion and even accidents. One of the challenges that has caught the attention in the area of research of prevention of traffic accidents, it is the generation of mechanisms that contribute to monitoring and evaluating the driver behavior. This paper presents a prediction model based on Machine Learning techniques to detect cognitive states based on the monitoring of the electroencephalographic signals of drivers of vehicles acquired in various real driving scenarios. The proposed prediction model consists of three phases: 1. - Acquisition of electroencephalographic signals from drivers in real time. 2. - Select and extract the main characteristics of the signals. 3. - Develop the prediction model using the Support Vector Machine (SVM) algorithm. Finally, to evaluate the performance of the proposed model, it was compared with two Machine Learning Techniques: K-Nearest-Neighbors (KNN) and Logistic Regression (LR). The results obtained through the experiments demonstrate that the proposed model has the best performance in the evaluation and prediction of the cognitive workload of the physiological signals of the conductors, with a 93% accuracy in the classification of the information compared to other models that have an 84% and 54% accuracy in the classification.
A Misbehavior Authority System for Sybil Attack Detection in C-ITS

Global misbehavior detection is an important backend mechanism in Cooperative Intelligent Transport Systems (C-ITS). It is based on the local misbehavior detection information sent by Vehicle's On-Board Units (OBUs) and by Road-Side Units (RSUs) called Misbehavior Reports (MBRs) to the Misbehavior Authority (MA). By analyzing these reports, the MA provides more accurate and robust misbehavior detection results. Sybil attacks pose a significant threat to the C-ITS systems. Their detection and identification may be inaccurate and confusing. In this work, we propose a Machine Learning (ML) based solution for the internal detection process of the MA. We show through extensive simulation that our solution is able to precisely identify the type of the Sybil attack and provide promising detection accuracy results.
Vitamin D deficiency and its association with non communicable diseases

Vitamin D Deficiency (VDD) has become a worldwide public health problem. Globally about one billion people suffer from VDD. It is well known that vitamin D has an established role in skeletal growth, development and its maintenance. Adequate vitamin D status is also important for optimal function of many organs and tissues throughout the body. Vitamin D receptors are present on a large variety of cell types, including myocytes, cardiomyocytes, pancreatic beta-cells, vascular endothelial cells, neurons, immune cells and osteoblasts. Deficiency of vitamin D leads to skeletal disorders like rickets in children and osteomalacia in adults. Recent evidence suggest that in addition to skeletal disorders VDD is also responsible for exacerbating various non-communicable diseases such as obesity, hypertension, cardio vascular disease, diabetes mellitus, metabolic syndrome, cancer, neurological disorders etc. Due to paucity of scientific evidence on the role of vitamin D in the manifestation of these non-communicable diseases, it becomes necessary to carry out further research and establish the association of VDD with these diseases.
Exploiting Error-Correction-CRC for Polar SCL Decoding: A Deep Learning-Based Approach

We investigate the cyclic redundancy check (CRC) codes aided successive cancellation list (SCL) decoding schemes to improve the performance of Polar codes. Distinguished from the existing literatures of Polar codes that only consider the error detection capability of CRC, we attempt to take advantage of the inherent error correction capability of CRC and we first devise a segmented CRC-error-correcting aided SCL decoding (SCC-SCL) framework. Based on this framework, an error-correcting table based SCC-SCL (ET-SCC-SCL) decoding scheme is proposed, by introducing the look-up-table based CRC error correction method into the segmented Polar SCL decoding process. Since the error correction capability is limited by the size of the look-up table, which in turn limits the performance gain, we further propose a deep learning based SCC-SCL (DL-SCC-SCL) decoding scheme. In this scheme, a long short-term memory (LSTM) network replaces the error correction table to perform error correction, combining the sequence of log likelihood ratios (LLRs) with the syndromes to determine the error patterns. Simulation results show that both of the proposed decoding schemes have significant performance gain over the classic CRC error-detection aided SCL decoding scheme. Especially for the DL-SCC-SCL, the performance gain at bit error rate of 10−5 is about 0.5 dB.
Stacking Sequence Retrieval of Large Composite Structures in Bi-step Optimization Strategies Using Mechanical Constraints

Multi-step optimization of large composite structures involves formulating the stiffness distribution in terms of lamination parameters that provide a continuous design space, followed by a second optimization in the discrete design space to obtain equivalent stacking sequences. The retrieval of stacking sequences is often challenging as a result of additional manufacturing constraints such as blending in the discrete design space which are not factored into the initial continuous optimization. By subjecting the continuous optimization to blending constraints, a better match between the discrete and continuous design spaces is achieved which in turn improves the retrieval of stacking sequences with equivalent stiffness and mechanical behaviour. An open-source Python application capable of multi-component optimization was developed in this thesis in order to revisit the application of lamination parameter matching objective functions in the discrete step. By subjecting these objectives to mechanical constraint penalties, the vicinity of reference continuous optimum could be explored more effectively to steer the discrete solutions towards feasible design regions. A comparison was made between discrete optimization according to blended and unblended continuous designs points, where it was found that the former translated to improvements in the retrieved stacking sequences with a considerable decrease in weight versus target continuous references without blending constraints of an aircraft wing.
Guiding Search in Relational Pathfinding-based Concept Discovery via Bivariate Statistical Methods

Relational pathfinding-based systems learn concept descriptors by extending candidate concept descriptors by one literal at a time. As such learning systems usually deal with large search spaces, choosing literals to extend candidate concept descriptors becomes an essential issue. In this study we empirically analyze applicability of three bivariate statistical methods namely, frequency ratio, hazard index, and weight of evidence, as heuristics to choose literals to extend candidate concept descriptors. 10-fold experiments conducted on three benchmark datasets showed that frequency ratio, hazard index, and weight of evidence were able to reduce the space and hence provided speedups when compared to extending candidate concept descriptors by a randomly chosen literal. Moreover, the heuristic-based settings provided improved predictive accuracy.
Sequential Encoding in Visual Working Memory: In the Absence of Structure, Recency Determines Performance

iii LIST OF TABLES.vii LIST OF FIGURES.viii CHAPTER 1. SEQUENTIAL ENCODING IN VISUAL WORKING MEMORY: IN THE ABSENCE OF STRUCTURE, RECENCY DETERMINES PERFORMANCE.1 Mechanisms of Working Memory.4 Verbal Domain.4 Visuospatial Domain.6 The Present Study.9 2. EXPERIMENT 1.12 Methods.12 Participants.12 Stimuli.12 Procedure.12 Results.15 Considering Set Size Only.17 Considering Lag as a Function of Set Size.17 Discussion.23 3. EXPERIMENT 2.27 Methods.27
Trip purpose identification using pairwise constraints based semi-supervised clustering

Clustering of smart card data captured by automated fare collection (AFC) systems has traditionally been viewed as an unsupervised method. However,some additional information about human behaviour is available in addition to the smart card data points that can facilitate better partitioning of the data. In this paper, such prior knowledge is translated into pairwise constraints and used with the COPKMEANS clustering algorithm to identify user activities. The effectiveness of the method was evaluated using performance evaluation measures by comparison of the results with the ground truth. The results demonstrate that pairwise constraints significantly enhance the accuracy of the clusters.
Artificial neural network technologies as a tool to histological preparation analysis

Food fraud has become one of the great internal and external threats nowadays. For effective and objective detection of adulterated products, real-time control systems are used.Microstructural analysis is one of these methods. It allows rapid quality and composition assessment of food’s raw materials and the finished products. Human skills and expertise/involvement in interpreting the resultsof microstructural analysis is still absolutely necessary, despite the possibility this is subjective. Modern hardware/software systems for histological analysis with neural network technologies will exclude the subjectivity of that result interpretation. For convolutional neural network operating, the “learning with the teacher” model is used. With this training method, a set of data is prepared, whichacts as a series of observations, and for which the values of the input and output variables are indicated, such as: histological analysis data > conclusion about the sample composition > adulteration definition. The network learns to establish connections between them. This paper presents the classification parameters for vegetable food component identification as a part of meat raw materials and finished products. A unified database structure has been developedfor structuring and summarizing the main microstructural characteristics of histological sections for various types of meat raw materials. The production-rule systems of “If… then …else” are given, and vegetable protein components were chosen as an example. Based on these rules, training of convolutional neural network occurs.
Idiopathic Spontaneous Intestinal Perforation in Neonates; Explorative Laparatomy or Primary Peritoneal Drainage?

Objective: To highlight our management of idiopathic spontaneous intestinal perforation (ISIP) in two medical teaching hospitals. Methods: Retrospective study was done between Sept. 2003-Sept 2011. Results: The total number of patients was 9. The gestational age ranged between 27-29 weeks. The age at presentation ranged between 8-21 days. All the patients were males. Clinically there was severe tympanic abdominal distention. Radiologically there was severe pneumoperitonium. Operatively there was single perforation with normal surrounding bowel. The treatment was primary explorative laparotomy in 4/9 patients and primary peritoneal (PPD) drainage in 5/9 patients. The total survival rate was 77.7%. The total morbidity rate was 25%. The morbidity and mortality rate after PPD were zero. Conclusions: ISIP is a serious condition associated with a high mortality rate if not promptly recognized and treated. To overcome the high mortality, PPD is a good option to rescue and stabilize the patient before the definitive treatment.
Statistical prediction of typhoon‐induced accumulated rainfall over the Korean Peninsula based on storm and rainfall data

A statistical model for typhoon‐induced accumulated rainfall (TAR) prediction over the Korean Peninsula using track, intensity and rainfall data of 91 typhoons affecting the peninsula during the period 1977–2014 is developed. The statistical estimation of the TAR consists of three steps: (1) estimating the TAR at 56 observational weather stations for the 91 typhoons; (2) selecting typhoons whose tracks are similar to that of the target typhoon within the area of 32–40 ° N and 120–138 ° E using a fuzzy C‐mean clustering method; and (3) calculating the mean TAR for the 16 selected typhoons based on track similarity after an intensity correction of the TAR using a linear regression between the TAR and intensity anomaly. To validate the model, real case predictions were performed on typhoons Chan‐hom and Goni in 2015 and compared with the observed TARs as well as with those from local and global operational models. The result showed that when the best‐track data are used, the present statistical model can predict the TAR with the accuracy of mean root mean square errors (RMSEs) of 33 mm (Chan‐hom) and 29 mm (Goni) at the 56 stations, which were much less than the results of the local model. With the predicted track and intensity data for the two typhoons, the present model also showed an overall good performance with RMSEs of 30–34 mm (Chan‐hom) and 29–49 mm (Goni), depending on the accuracy of the predicted track and intensity, which were generally less than the results of the global model.