Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Learning Media Prezi Integrated Problem Based Learning Model (PBL) to Improve Student Results High School

This study aims to determine: 1) the utilization of computer-based powerpoint learning media used in SMAN 1 Darul Falah has been in accordance with BSNP standards; 2) the utilization of integrated prezi learning media PBL model developed on salt hydrolysis discussion has been in accordance with BSNP standard; 3) differences in use of computer-based powerpoint learning media and prezi integrated learning media PBL model developed to improve student learning outcomes. The population in this study is all students of class XI IPA at SMAN 1 Darul Falah Academic Year 2017/2018. The sampling technique is purposive sampling. The sample of this research is two classes in SMAN 1 Darul Falah each 2 class. Research instrument in the form BSNP, objective test of valid learning result and reliable. Analytical technique used Two Way Anova technique and General Linear Model (GLM) test on SPSS 17.00 program. Analytical technique used Two Way Anova technique and General Linear Model (GLM) test on SPSS 17.00 program. The results concluded that: 1) The feasibility level of learning media based powerpoint computer used in SMAN 1 Darul Falah has a decent category; 2) The prezi integrated learning media PBL model developed has a very decent category; 3) there are significant differences in learning outcomes using computer-based powerpoint learning media and prezi integrated learning media PBL model developed. KeywordsPrezi Learning Media, Problem Based Learning Model (PBL), Increased Learning Outcomes, Salt Hydrolysis.
Autonomous Sensorimotor Learning for Sound Source Localization by a Humanoid Robot

We consider the problem of learning to localize a speech source using a humanoid robot equipped with a binaural hearing system. We aim to map binaural audio features into the relative angle between the robot's head direction and the target source direction based on a sensorimotor training framework. To this end, we make the following contributions: (i) a procedure to automatically collect and label audio and motor data for sensorimotor training; (ii) the use of a convolutional neural network (CNN) trained with white noise signal and ground truth relative source direction. Experimental evaluation with speech signals shows that the CNN can localize the speech source even without an explicit algorithm for dealing with missing spectral features.
Caching to the Sky: Performance Analysis of Cache-Assisted CoMP for Cellular-Connected UAVs

Providing connectivity to aerial users, such as cellular-connected unmanned aerial vehicles (UAVs) or flying taxis, is a key challenge for tomorrow's cellular systems. In this paper, the use of coordinated multi-point (CoMP) transmission along with caching for providing seamless connectivity to aerial users is investigated. In particular, a network of clustered cache-enabled small base stations (SBSs) serving aerial users is considered in which a requested content by an aerial user is cooperatively transmitted from collaborative ground SBSs. For this network, a novel upper bound expression on the coverage probability is derived as a function of the system parameters. The effects of various system parameters such as collaboration distance and content availability on the achievable performance are then investigated. Results reveal that, when the antennas of the SBSs are tilted downwards, the coverage probability of a high-altitude aerial user is upper bounded by that of a ground user regardless of the transmission scheme. Moreover, it is shown that for a low signal-to-interference-ratio (SIR) threshold, CoMP transmission improves the coverage probability for aerial users from 10% to 70% under a collaboration distance of 200 m.
Sound and music recommendation with knowledge graphs [dataset]

Music Recommendation Dataset (KGRec-music). Number of items: 8,640. Number of users: 5,199. Number of items-users interactions: 751,531. All the data comes from songfacts.com and last.fm websites. Items are songs, which are described in terms of textual description extracted from songfacts.com, and tags from last.fm. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ". txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. Multiword tags are separated by -. The name of the file is the id of the item plus the ". txt" extension. Not all items have tags, there are 401 items without tags. implicit_lf_dataset. txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: user_id /t sound_id /t 1 /n. Sound Recommendation Dataset (KGRec-sound). Number of items: 21,552. Number of users: 20,000. Number of items-users interactions: 2,117,698. All the data comes from Freesound.org. Items are sounds, which are described in terms of textual description and tags created by the sound creator at uploading time. Files and folders in the dataset: /descriptions: In this folder there is one file per item with the textual description of the item. The name of the file is the id of the item plus the ". txt" extension. /tags: In this folder there is one file per item with the tags of the item separated by spaces. The name of the file is the id of the item plus the ". txt" extension. downloads_fs_dataset. txt: This file contains the interactions between users and items. There is one line per interaction (a user that downloaded a sound in this case) with the following format, fields in one line are separated by tabs: /nuser_id /t sound_id /t 1 /n.
IOT – Attacks and Challenges

Internet of Things (IoT) is becoming a big boom in the wireless networking. This is connectivity among different entities or things. As the devices are directly connected to each other to share the information there occurs a challenging issues in security.This is a study paper which focuses on the vulnerabilities and various attacks in each layer of the IoT.
Novel computer‐aided diagnosis of lung cancer using bag of visual words to achieve high accuracy rates

Lung Cancer is considered as one of the deadliest diseases among other lung disorders. Several researchers studied and investigated about classifying the lung cancer images and their approaches do not provide better accuracy. The objective of this paper is mainly focused on detecting the cancerous region and classifying the lung cancer CT images. The proposed work plays a significant role in detecting the types of cancerous region in the lung CT images and helps radiologists to enhance the diagnosis in medical care of patients. It's also a less time consuming process in classifying the lung CT images. Bag of Visual Words (BoVW) is a novel classification technique which is applied on lung CT images to classify the cluster of begnin or malignant lung cancer in different steps. With the help of MATLAB 2016, the simulation results are obtained. The achieved accuracy of BoVW classifier is 96% which is higher than other classifiers applied on lung cancer CT images. This classifier highly reduces the computational complexity. This CAD system is very beneficial to radiologists and oncologists in cancer research centres or any cancer institutions. By applying other segmentation or novel classification techniques may improve the automatic diagnosis of lung cancer images.
Online temporal detection of daily-living human activities in long untrimmed video streams

Many approaches were proposed to solve the problem of activity recognition in short clipped videos, which achieved impressive results with hand-crafted and deep features. However, it is not practical to have clipped videos in real life, where cameras provide continuous video streams in applications such as robotics, video surveillance, and smart-homes. Here comes the importance of activity detection to help recognizing and localizing each activity happening in long videos. Activity detection can be defined as the ability to localize starting and ending of each human activity happening in the video, in addition to recognizing each activity label. A more challenging category of human activities is the daily-living activities, such as eating, reading, cooking, etc, which have low inter-class variation and environment where actions are performed are similar. In this work we focus on solving the problem of detection of daily-living activities in untrimmed video streams. We introduce new online activity detection pipeline that utilizes single sliding window approach in a novel way; the classifier is trained with sub-parts of training activities, and an online frame-level early detection is done for sub-parts of long activities during detection. Finally, a greedy Markov model based post processing algorithm is applied to remove false detection and achieve better results. We test our approaches on two daily-living datasets, DAHLIA and GAADRD, outperforming state of the art results by more than 10%.
A new serotype of infectious bronchitis virus responsible for respiratory disease in Arkansas broiler flocks.

Sixteen virus isolates from outbreaks of respiratory disease in Arkansas broilers in February, March, and April of 1972 were examined by the constant-serum variable-virus serum-neutralization test in tracheal culture. None were the Mass 41, Conn, Iowa 97, Iowa 609, Holte, JMK, Clark 333, SE-17, or Florida serotype of infectious bronchitis virus (IBV). Fourteen were neutralized by an antiserum prepared with one, designated Arkansas 99, to a degree similar to the homologous virus. Ark 99 has the morphology of a coronavirus, and signs in eggs and its neutralizing characteristics suggest that it is a previously unreported serotype of IBV.
Balanced Error Distribution for Internet Video Retargeting

With the progress of network technology, online video is widely used by users and popular. Image and video retargeting techniques have been proposed in the recent past by researchers around the world, low-complexity algorithms allow video to be transmitted more quickly. Our Balanced Error Distribution (BED) retargeting method follows the same concept like uniform scaling, but only removes unimportant regions, balancing main objects’ error distribution. Priority map is proposed in this paper to maintain the structure of straight lines and irregular shape of objects without deforming complex image contents, which may be altered in traditional seam carving for complex image or video. In addition, the proposed mechanism adopts the general temporal coherence algorithm with strict condition (two continuous seams must be close as possible) to maintain visual continuity, such that the resulting video will not look shaky due to sudden changes in the background. The proposed method not only resizes the video with the retention of important information but also maintains the structural properties of objects in different kinds of videos.
An opposition-based chaotic Grey Wolf Optimizer for global optimisation tasks

ABSTRACT Real-world optimisation problems that are not endowed in mathematical characteristics like differentiability, convexity etc. require non-traditional optimisation approaches that explore the promising regions of the search space stochastically to achieve the optima of the problem. Grey Wolf Optimizer (GWO) is one of the efficient and recently developed approaches in the area of Swarm Intelligence to solve real-world optimisation problems over continuous space. However, in some cases, due to the insufficient diversity, GWO still suffers from the problem of stagnation in local optimums. Therefore, this article presents the novel algorithm OCS-GWO that enhances the performance of original GWO by introducing the opposition-based learning to approximate the closer search candidate solution to the global optima and chaotic local search for the exploitation of the search regions efficiently. In OCS-GWO, a chaotic local search is used for balancing the exploration and exploitation operators that are the underlying features of any stochastic search algorithm. The performance of the proposed algorithm OCS-GWO has been evaluated on a set of 23 standard benchmark test problems and on three engineering application problems – gear train, cantilever beam and speed reducer design problems. The experimental results on test problems and engineering applications confirm the efficiency and reliability of the proposed algorithm over original GWO.
The generation of synthetic handwritten data for improving on-line learning

In this paper, we introduce a framework for on-line learning of handwritten symbols from scratch. As such, learning suffers from missing data at the beginning of the learning process, in this paper we propose the use of Sigma-lognormal model to generate synthetic data. Our framework deals with a real-time use of the system, where the recognition of a single symbol cannot be postponed by the generation of synthetic data. We evaluate the use of our framework and Sigma-lognormal model by comparison of the recognition rate to a block-learning and learning without any synthetic data. Experimental results show that both of these contributions represent an enhancement to the on-line handwriting recognition, especially when starting from scratch.
Discrete time H∞ control

A representation formula for all controllers for a given discrete-time system which satisfy an H∞ norm bound is given. The approach is based on the theory of linear quadratic difference games, with the time-varying, finite-horizon version of the problem being the main focus of attention. It is found that the controller representation formula can be specified in terms of a pair of discrete-time game-theoretic Riccati difference equations.
Species diversity and distribution of Ostracoda (Crustacea) in mesosaline Lake Bafa (Aegean Region, Turkey)

This study was carried out in order to determine Ostracoda fauna of Lake Bafa (Aydın-Muğla, Turkey) and investigate their distribution from 20 sites in February, May, July and September of 1997. The ostracods assemblages comprised a mixture of 14 freshwater and brackish-marine origin species. The most common and abundant species were the brackish-water ostracods Cyprideis torosa and Loxoconcha elliptica. According to Shannon-Weiner diversity index, it was found that Lake Bafa had a mean diversity value of H’= 0.85. The ostracod abundance showed an increase in the warmer period starting from late spring and reached maximum values in May. We analysed the dominance and distribution of ostracod assemblages and their relation to environmental factors (temperature, dissolved oxygen, pH, and salinity) by using classification and ordination techniques. Canonical correspondence analysis (CCA) was used to characterize the relationship between ostracods and the environmental variables. According to the first axis of CCA, we determined salinity to be the most important environmental variable affecting ostracod species distribution. According to Jaccard similarity coefficient (Jaccard Index) analysis results, sampling stations and ostracod species assemblage were clustered into two groups with the non-weighted pair-group method with arithmetic mean based on presence/absence of species. Besides multivariate analysis, Spearman correlation analysis indicated that some species (Cyprideis torosa. torosa, Loxoconcha elliptica, Cytherois fischeri, and Leptocythere lacertosa) had a positive correlation with salinity, whereas there are strong negative correlations between salinity and other nine species, which are, more related to freshwater environments.
Field Efficacy of Parasitoid, Coccophagus scutellaris (Hymenoptera: Aphelinidae) and the Predator, Exochomus flavipes (Coleoptera: Coccinellidae) against Pulvinaria Psidii (Hemiptera: Coccidae) in Egypt

The aim of this study was to investigate the biological control of the green shield scale, Pulvinaria psidii Maskell (Hemiptera: Coccidae), on guava trees in Gahrbiya, Egypt, by the release of two of its natural enemies. For this purpose, 12 releases of larval Exochomus flavipes (Thunberg) (Coleoptera: Coccinellidae) and Coccophagus scutellaris (Dalman) (Hymenoptera: Aphelinidae) were carried out on guava trees between May 2007 and April 2008. These two beneficials were reared under defined climatic conditions (25-27°C and 65-75%) in a laboratory. A significant reduction in the number of P. psidii populations was recorded as compared with control plots. Six months after the release of E. flavipes , the average populations of P. psidii decreased from 1897/30 to 475/30 leaves in the treated plot. With regard to C. scutellaris, the number of parasitized P. psidii nymphs in the treated plots increased from 2% to 54% as compared to the control plots. The present study indicated that C. scutellaris and E. flavipes are potential biocontrol agents of P. psidii on guava.
Development of Dual-Band Antenna for Wireless Applications Pankaj Patil

The multiband antenna technology has significant impact on today’s advance wireless communication networks. The paper presents the design aspects along with analysis of a planar compact dual-band antenna for fulfilling the requirements of wireless applications. The optimized dimension of the designed antenna model is (28 x 36 x 1.6) mm. The insertion of asymmetric L-shaped strips with variation in dimension such as the length, thickness and a space within the strips have considerable effect on the frequency of resonance of the antenna. The optimization of asymmetric strips and slots is done by performing number of iterations. The prototype is fabricated using FR-4 substrate. The measured result shows that the antenna is operating in 3.4-3.60 GHz and 5.2-5.85 GHz. The resultant frequency ranges are suitable for WiMAX (3.5 GHz) and Wi-Fi/WLAN/HiperLAN (5.5 GHz) bands respectively.
Deep Learning Applied to Automatic Anomaly Detection in Capsule Video Endoscopy

Introduction: Regularly screening of the gastrointestinal tract for polyps is the an important measure for preventing colorectal cancer. Screening large population’s gastrointestinal tract is with todays common methods too time consuming and expensive to accomplish. In this thesis explore the possibilities of using capsule video endoscopy (CVE) in combination with state of the art convolutional neural network (CNN) to create a computer aided diagnosis-system for automatic classification of diseases in the gastrointestinal tract. Methods: First we create a dataset using images extracted from real CVE examinations of 19 patients. We use tensorflow framework to train and evaluate different state of the art CNNs compare with each other. We also propose a new CNN consisting of two state of the art CNNs in a parallel architecture. We also develop a Graphical User Interface (GUI) aimed at medical doctors for classifying VCE data. Results: The dataset created contains 3267 labeled images with highlighted lesion annotation. We achieve FMacro-score, precision, recall and accuracy at 0.547, 0.553, 0.548 and 0.682 respectively on the best performing CNN. The GUI prototype is a simple application which classifies images based on predictions done by the trained network. Discussion: The dataset have some balance issues as the largest class, normal images contains more than thousand samples, and five of the diseases contains less than hundred. The CNNs show promising preliminary results suggesting that it’s a feasible approach. The GUI prototype suffers from severe performance issues and is only able classify about two images per second. Conclusion: In this thesis we have shown that using CNN in combination with CVE is a a feasible approach. The results are promising but more research is required. The big challenge in image classification continues to be having a large and reliable dataset.
Reply

We read with great interest the recent article by Atiq et al. By assessing benefits and harms from hepatocellular carcinoma (HCC) surveillance, they found that over one-fourth of patients with cirrhosis experienced physical harm due to false-positive or indeterminate surveillance tests, which were more often related to ultrasound than alpha-fetoprotein. They pointed out that high false-positive or indeterminate results of ultrasound may induce unnecessary computed tomography or magnetic resonance imaging scans, which would cause some degree of physical harm, such as radiation exposure. We have the following concerns. It cannot be neglected that a major limitation of this study was its retrospective nature. All 680 patients with cirrhosis stratified either to the surveillance or nonsurveillance groups were determined by researchers only after review of medical records. As the authors describe, “ultrasounds with indications including ‘surveillance,’ ‘screening,’ ‘rule out HCC,’ and ‘cirrhosis’ were classified as surveillance exams. ” However, the authors did not explain whether these patients underwent their regular or scheduled screening tests at every 6-month interval according to the recommendations from the American Association for the Study of Liver Diseases and the National Comprehensive Cancer Network. As a matter of fact, if a rigorous standard is not set for screening interval (for example, undergoing a screening test once a year or more), these tests should not be termed as surveillance. This may explain why the detection rate of early HCC, even in those patients under “surveillance” was far from satisfactory in this study. In a word, the bias caused by stratification error may in all probability lead to an incorrect conclusion, and the reliability of this conclusion warrants careful interpretation and further validation. In summary, clarification regarding the aforementioned omission would greatly solidify the conclusions made by Atiq et al. in their study.
THE HISTOLOGY AND MUCIN HISTOCHEMISTRY OF THE FARMED JUVENILE AFRICAN CATFISH DIGESTIVE TRACT (Clarias gariepinus B)

The microanatomy of the juvenile African catfish was studied. This study was carried out to provide baseline study on the digestive tract of this important culture seed in the commercial catfish aquaculture in Nigeria, as there is dearth of information on it from available literature to help understand its digestive physiology. The result showed that the oro-pharyngeal wall and tongue were lined by stratified squamous epithelium but the tongue lacked taste buds, thus making it a non-gustatory organ. The lamina propria contained irregular collagen fibres. The oesophageal longitudinal folds was lined by stratified mucous epithelium containing eosinophilic club cells, with the lamina propria core containing regular collagen fibres. The oesophageal tunica muscularis contained skeletal muscles in an inner longitudinal and outer circular arrangement. The stomach was lined by simple columnar epithelium with apical neutral mucin. The gastric epithelium contained intraepithelial lymphocytes. The stomach cardiac and fundic regions contained gastric glands in the lamina propria while none was seen in the pyloric region. The intestinal simple absorptive epithelium contained intraepithelial lymphocytes and goblet cells that increased in number towards the rectum. Mucin histochemistry revealed the presence neutral mucin in the oesophageal mucous cells and this is associated with pre-gastric digestion. Acid mucin present in the intestinal goblet cells are involved with the local defense against pathogenic organisms in the digestive tract. This study provides data for further investigative researches and will help clinicians in diagnosis of its diseases thus helping aquaculture growth.