Reviewerly/exHarmony huggingface dataset:
A deep learning based methodology for video anomaly detection in crowded scenes

Different approaches have been proposed in the literature for anomaly detection in image/video area. Traditional methods such as trajectory or spatio-temporal based techniques rely on hand-crafted features. The occlusion problems and high complexity in crowded scenes are the vital drawbacks behind using these methodologies. Deep learning structures proved recently to be useful for defining effective solutions for anomaly detection where the high level features are learnt and selected automatically. However, the block-wise methods such as CNNs are computationally slow. On the other hand, they are totally supervised learning methodologies, while the video-based anomaly detection is an unsupervised problem. Auto- Encoders (Convolutional AE, vibrational AE, etc.) can be considered as an alternative option. This paper presents a stateof- the-art deep learning algorithm to be applied in such an unsupervised problem. Using the basic concepts behind the Auto-Encoders as a well-known unsupervised learning algorithm, we propose a novel methodology to detect and localize the anomalies in a video scene. The presented network is trained based on the normal patterns during training phase. The proposed structure enables the system to capture the 2D structure in image sequences during the learning process. The working hypothesis is that a deep network is able to learn normal events in videos, and, therefore, the difference of normal and anomalous frames can be used for devising an anomaly score. The simulation results on well-known data sets such as UCSD confirm that the proposed methodology achieves high performance results in case of accuracy and total processing time compared with counterpart approaches.
ContainerStress: Autonomous Cloud-Node Scoping Framework for Big-Data ML Use Cases

Deploying big-data Machine Learning (ML) services in a cloud environment presents a challenge to the cloud vendor with respect to the cloud container configuration sizing for any given customer use case. OracleLabs has developed an automated framework that uses nested-loop Monte Carlo simulation to autonomously scale any size customer ML use cases across the range of cloud CPU-GPU "Shapes" (configurations of CPUs and/or GPUs in Cloud containers available to end customers). Moreover, the OracleLabs and NVIDIA authors have collaborated on a ML benchmark study which analyzes the compute cost and GPU acceleration of any ML prognostic algorithm and assesses the reduction of compute cost in a cloud container comprising conventional CPUs and NVIDIA GPUs.
MENGEMBANGKAN FRAMEWORK IMPLEMENTASI SISTEM OTOMASI PERPUSTAKAAN BERBASIS INTERNET OF THINGS : DEVELOPING FRAMEWORK IMPLEMENTATION OF IOT-BASED LIBRARY AUTOMATION SYSTEM

Today Information and Communication Technology (ICT), such as the Internet of Things (IoT), has a decisive influence on all aspects of human life. IoT as a dominant phenomenon is a transformation of daily life through the use of intelligent functions such as Radio Frequency Identification (RFID) and Wireless Sensor Technology Networks (WSN). Along with the development of IoT in size and dimensions thus increasing many community contexts; like, a traditional library system. This study proposes an execution framework on the use of IoT to update conventional library structures and schemes to smart online systems. IoT enables real-time connectivity of physical objects (such as books or other types of text) through the use of RFID tags and sensors. Monitoring books in real time and geographic location of objects are some of the characteristics of using IoT tags. These IoT features enable the implementation of library staff through supply chains, integration with various types of technologies such as databases, data collection, and cloud systems. The Internet of Things also offers an overview of links between a large number of universities and libraries in the world in real time and at any time. It was concluded that the application of IoT in library management systems became a promising structure that could play an important role in organizing human knowledge and in accessing information to help researchers, designers and officers in more efficient and intelligent ways.
Development of Intrusive Uncertainty Quantification Method Based on Edge Detection Technique

A new intrusive uncertainty quantification method, namely a polynomial annihilation-based stochastic Galerkin method, is proposed to efficiently capture discontinuous system responses in stochastic space. The proposed method is based on the multi-element method and the polynomial annihilation edge detection, and achieves a minimal and optimal decomposition of the stochastic space so that the computational cost is minimized. The performance of the proposed method is demostrated on a one-dimensional test problem whose solutions exhibit discontinuous behavior in stochastic space: an ordinary differential equation problem for uncertainty propagation. The results obtained for the test problem show that the proposed method is consistently more accurate and more efficient than the conventional stochastic Galerkin method even when the solution contains a discontinuity in the stochastic space.
EFEKTIVITAS PENGGUNAAN LABORATORIUM BAHASA DALAM PEMBELAJARAN KETERAMPILAN MENYIMAK BAHASA JERMAN MAHASISWA PRODI PENDIDIKAN BAHASA JERMAN FBS-UNM

Abstract. This research is quasy experiment , two group pretest - posttest design that aims to determine the effectiveness using the language laboratory learning listening skills in German language students , German Study Program, Faculty of Language and Literature, Makassar State University. The population in this study were students on the second semester, academic year 2015, German language education students, totaling 22 people , and the sample used in this study is total sample . Hypothesis testing using t- test. Analysis using t-test, tcount = 2.42 while t tabel= 2.086 t count ≥ t table (2.42 ≥ 2.086 ) . So, H0 stating that use of the language laboratory is not effective in listening skills German student education is rejected. Consequently H1 which states that the use of effective language laboratory in the listening skills of students in the education department of the German language is accepted. The results of this study can be concluded that teaching by using a language laboratory can be used as a medium of learning to improve listening skills of students in the German language.
Restoration of Poisson-Gaussian noise corrupted images using Euler-Maclaurin approximation

Based on an established statistical model in the literature, we set up a likelihood function of an image field having both Poissonian and Gaussian components. This likelihood function is an infinite series and is approximated by an integral alongwith Bernoulli number correction terms. The approximate expression for the likelihood function is accomplished by Mean value theorem. Subsequently, upon maximization of the likelihood function yields a good estimate of the noiseless image field. The rationality behind our work relies on the study of the variations in the proposed algorithm as a function of the Poissonian and Gaussian component variances. Finally, we justify the performance of our model using MATLAB simulations in the light of the asymptotic efficiency of the M.L.E (Maximum likelihood estimate) for the purpose of restoration of the original image.
A New Tone Mapping Operator Based on Two-layer Model

Tone mapping is a process of compressing high dynamic range image data into low dynamic range data according to specific rules, so that the image can be displayed on ordinary display devices. The specific rules are called tone mapping operators. Nowadays, the mainstream tone mapping operator can be divided into two kinds, the global operators and the local operators. This paper processes a new tone mapping operator based on two-layer model. Our operator combines the characteristics of global operators and local operators, and it can effectively compress the high dynamic range images with an excellent performance.
LibGuides: Free Resources for Remote Learning: Home

Here you can find resources that are being made freely available by publishers while students and educators move to online learning.
Motion-aware deep video coding network

Recent advances in deep learning have achieved great success in fundamental computer vision tasks such as classification, detection and segmentation. Nevertheless, the research effort in deep learning-based video coding is still in its infancy. State-of-the-art deep video coding networks explore temporal correlations by means of frame-level motion estimation and motion compensation, which require high computational complexity due to the frame size, while existing block-level interframe prediction schemes utilize only the co-located blocks in preceding frames, which did not consider object motions. In this work, we propose a novel motion-aware deep video coding network, in which inter-frame correlations are effectively explored via a block-level motion compensation network. Experimental results demonstrate that the proposed inter-frame deep video coding model significantly improves the decoding quality under the same compression ratio.
A semi-quantum extended B92 protocol and its analysis

Unlike purely classical communication, unconditionally secure key distribution is possible if Alice and Bob are both equipped with quantum hardware. The degree to which a protocol needs to be quantum is not only an interesting theoretical question, but also important for practical implementations. Indeed, one may wish to construct cheaper devices, or compensate for device malfunction. In this sense, studying limited resource QKD protocols is an important problem. One direction to studying this is the semi-quantum model introduced by Boyer et al. in 2007 (PRL 99 140501). Several provably secure semi-quantum protocols were put forth. However, most of these protocols were proven secure in the perfect qubit scenario and not necessarily against practical attacks. Only recently, starting with seminal work of Boyer, Katz, Liss, and Mor in (PRA 96 062335) has research in the field of semi-quantum cryptography considered practical devices and imperfections, such as multi photon sources and imperfect detectors. In this work, we present a new SQKD protocol based on an Extended B92 protocol which is able to counter certain practical attacks. Furthermore, the techniques we use may see broad application to other limited-resource (S)QKD protocols.
Efficacy of defending deep neural networks against adversarial attacks with randomization

Adversarial machine learning is concerned with the study of vulnerabilities of machine learning techniques to adversarial attacks and potential defenses against such attacks. Intrinsic vulnerabilities, incongruous and often suboptimal defenses are both rooted in the standard assumption upon which machine learning methods have been developed. The assumption that data are independent and identically distributed (i.i.d) samples implies training data are representative of the general population. Thus, learning models that fit the training data accurately would perform well on the test data from the rest of the population. Violations of the i.i.d assumption characterize the challenges of detecting and defending against adversarial attacks. For an informed adversary, the most effective attack strategy is to transform malicious data so that they appear indistinguishable from legitimate data to the target model. Current development in adversarial machine learning suggests that the adversary can easily gain the upper hand on this arms race since the adversary only needs to make a local breakthrough against the stationary target while the target model struggles to extend its predictive power to the general population, including the corrupted data. The fundamental cause of stagnation in effective defense against adversarial attacks suggests developing a moving target defense for a machine learning model for greater robustness. We investigate the feasibility and effectiveness of employing randomization in creating moving target defense for deep neural network learning models. Randomness is introduced through randomizing the input and adding small random noise to the learned parameters. Extensive empirical study is performed, covering different attack strategies and defense/detection techniques against adversarial attacks.
Box-particle Bernoulli Filter for Multipath Target Tracking in OTHR

This paper proposes a novel box-particle multipath Bernoulli filter (BP-MPBF) for the problem of single target tracking in Over-the-horizon radar (OTHR) under the clutter environment. The proposed algorithm combines the multipath Bernoulli filter (MPBF) with interval analysis to solve the problems of nonlinear measurement models and high computational complexity. First, the OTHR measurement model is established based on interval analysis, and then the box-particle filter is used to derive the BP-MPBF based on the original MPBF. Eventually, simulation results are provided to demonstrate the effectiveness of the BP-MPBF.
GENÇLERİN ÖLÜM ALGISI ÜZERİNE BİR İNCELEME

The study examines the effect of changing perception of death on the youth of our country in the west.The aim of this study is to investigate how the death practices differing in Western civilization are reflected in us and how much the practices related to death from ancient times have been applied by today's youth.The knowledge of young people about death and their approach to death were measured.The perception of death of the young generation, which is the most
LibGuides: Virtual Library Book Displays: APRIL - National Poetry Month

Check out our virtual book displays curated by student employees and librarians. This guide highlights a celebration of poetry and poetry.
RUSSIAN AND AMERICAN HORROR FICTION AS A GENRE, CREATIVE WRITING AND EDUCATIONAL PHENOMENON: A PROBLEM STATEMENT

Although the genre of horror has gained an extraordinary popularity in contemporary literature, it still raises controversy among specialists. The situation in Russia is especially complicated. Until the beginning of the 20th century, Russian horror fiction used to develop concurrently with the evolution of horror genre in the U.S., but after the revolution of 1917 and until the late 1980s this tradition was interrupted in Russia. Therefore, nowadays the question “What is horror fiction?” is unclear for Russian philologists, the question “How to write horror fiction?” is unclear for Russian writers, and including the horror genre in literature syllabus is regarded by Russian professors and teachers as a forbidden topic. The situation is different in the United States where a long-standing tradition of interpreting the category of the horrible has been created. Modern American scientists, philosophers, writers and educators agree that horror fiction in its best manifestations touches upon essential problems of a human soul. It allows to exert a powerful positive influence on the formation and development of a personality. Throughout the 20th century, the genre of horror was systematically evolving in the U.S., and as of today, it is American horror fiction that sets the standards of the genre all over the world. The aim of this research is to describe horror fiction as a dynamically developing genre from three points of view: 1) through comparative and genre analyzis of horror fiction in the U.S. and Russia; 2) by studying narrative strategies which are used by horror writers in the U.S.; 3) by surveying principles of teaching the horror genre in an American multicultural educational environment. After experiencing decades of oblivion, the genre of horror can revive in Russia thanks to the critical mastering of the U.S. experience, where the genre tradition has never been interrupted. A list of bibliography is attached to help beginner researchers with their study of the subject.
(1904-5168) Refining membership degrees obtained from fuzzy C-means by re-fuzzification

Fuzzy C-mean (FCM) is the most well-known and widely-used fuzzy clustering algorithm. However, one of the weaknesses of the FCM is the way it assigns membership degrees to data which is based on the distance to the cluster centers. Unfortunately, the membership degrees are determined without considering the shape and density of the clusters. In this paper, we propose an algorithm which takes the FCM clustering results and re-fuzzifies them by taking into account the shape and density of the clusters. The algorithm first defuzzifies the FCM clustering results. Then the crisp result is fuzzified again. Re-fuzzification in our algorithm has some advantages. The main advantage is that the fuzzy membership degrees of data points are obtained based on the shape and density of clusters. Adding the ability to eliminate noise and outlier data is the other advantage of our algorithm. Finally, our proposed re-fuzzification algorithm can slightly improve the FCM clustering quality, because the data points change their clusters by the similarity of the shape and density of their respective clusters. These advantages are supported by simulations for real and syntheticdatasets.
Effects of Laboratory Teaching On The Academics Performance of Biology Senior Secondary School Students In Hadejia LGA of Jigawa State

This study examined the effects of Laboratory teaching method on the AcademicPerformance of Senior Secondary Biology students in Hadejia LGA of JigawaState. A Quasi experimental pretest and post test design was employed for thestudy with a sample of 30 students used which are selected using purposivesampling technique. A self-constructed Biology Achievement Test (BAT) based ontwo (2) Biology Topics and using SSCE practical questions as a guide was used fordata collection. While the experiment group was taught using Laboratory teachingapproach, the control group were taught using lecture method for a period of threeweeks. The data collected were analyzed using mean score and t test statistic toanswer the 2 Research questions and hypothesis raised in the study. The resultsshowed that the Experimental group performed better with higher means post testscore in the BAT and the male score is also found to be higher than that of thefemale students. Based on the findings it was recommended that the scienceteachers should always make use of laboratory method for the students duringpractical’s, more efforts should be made to devote more hours to laboratory workand the Biology laboratory should be made more functional by making all thenecessary apparatus, specimen, chemicals, equipment and other materials availablesufficiently for students’ use.
Efficient quantile tracking using an oracle

Abstract Concept drift is a well-known issue that arises when working with data streams. In this paper, we present a procedure that allows a quantile tracking procedure to cope with concept drift. We suggest using expected quantile loss, a popular loss function in quantile regression, to monitor the quantile tracking error, which, in turn, is used to efficiently adapt to concept drift. The suggested procedures adapt efficiently to concept drift, and the tracking performance is close to theoretically optimal. The procedures were further applied to three real-life streaming data sets related to Twitter event detection, activity recognition, and stock trading. The results show that the procedures are efficient at adapting to concept drift, thereby documenting the real-world applicability of the procedures. We further used asymptotic theory from statistics to show the appealing theoretical property that, if the data stream distribution is stationary over time, the procedures converge to the true quantile.
Community development, well-being and technology: a Kenyan village

The chapter will look at the experiences of introducing electricity and broadband technology to a Kenyan village as a development project. Community development here includes well-being – in its economic, social and cultural dimensions. We consider how the technology was taken up in the rural village and integrated into daily life including how technology was used and not used. Electricity was used mainly for particular activities to do with leisure and communications, including cultural and social activities, although the enhancement of some local businesses suggested that there would be a greater economic impact further down the line. Broadband, on the other hand, had yet to make an impact. The chapter argues for the understanding of technology and its contribution to well-being outcomes of community development in Africa and not just in the global North.
10^6 parallelism in fourier-optics-based convolutional processor for network edge computing (Conference Presentation)

Performing feature extractions in convolution neural networks for deep-learning tasks is computational expensive in electronics. Fourier optics allows convolutional filtering via dot-product multiplication in the Fourier domain similar to the distributive law in mathematics. Here we experimentally demonstrate convolutional filtering exploiting massive parallelism (10^6 channels, 8-bit at 1kHz) of digital mirror display technology, thus enabling 250 TMAC/s. An FPGA-PCIe board controls the ‘weights’ and handles the data I/O, whereas a high-speed camera detects the inverse-Fourier transformed (2nd lens) data. Gen-1 processes with a total delay (including I/O) of ~1ms, while Gen-2 at 1-10ns leveraging integrated photonics at 10GHz and changing the front-end I/O to a joint-transform-correlator (JTC). These processors are suited for image/pattern recognition, super resolution for geolocalization, or real-time processing in autonomous vehicles or military decision making.
Uniaxial fringe projection profilometry using projection defocus

This paper introduces a novel uniaxial fringe projection profilometry (FPP) called active shape from projection defocus profilometry (ASPDP), which utilizes the sharpness analysis of binary fringe patterns to quantify the defocus level. Compared with previous uniaxial FPP methods, our work first utilizes a pinhole defocus model to account for three-dimensional reconstruction. Since defocused fringe pattern can be modeled as the original pattern convoluted with a point spread function (PSF), pixel-wise defocus level can be quantified with this PSFs kernel using temporal Fourier analysis. In this research, calibration is achieved by using a mechanical translation device, and determined by rational polynomial fitting to establish defocus-depth relationship. The experiment demonstrates that this method can provide an accurate reconstructed 3D geometry without shadows.