Reviewerly/exHarmony huggingface dataset:
Atmospheric precipitation as a source for the formation of surface water resources of the desert zone

Abstract The purpose of this article is to scientifically substantiate the metrological aspects of the possible volumes of surface runoff throughout its entire territory, which could be used in the interests of agricultural production. The article discusses the monthly norms of atmospheric precipitation at the meteorological stations of the Kyzyl Kum desert. It has been determined that in the Kyzyl Kum desert up to 75% of atmospheric precipitation falls in liquid form. Accordingly, the share of solid and mixed precipitation accounts for about 25%, and this ratio regularly changes with the movement from north to south. At present, the conditions for the formation and regularities of the spatial distribution of surface water resources of the Kyzyl Kum desert are unknown and, in the author’s opinion, it is economically inexpedient to neglect them, especially in dry years. The study used the most developed method of correlation functions to estimate the possible error in extrapolating the sum of atmospheric precipitation to a given area, but with respect to the entire set of meteorological stations available in the Kyzyl Kum desert. The results of the study showed a high stability of the ratios of monthly and maximum daily amounts of atmospheric precipitation. This stability takes place both in relation to the entire spring time and throughout the entire area of the Kyzyl Kum desert. Such stability and calculated ratios make it possible to roughly determine the daily precipitation amount, having only data on their monthly amount.
Juxtaposition of ideology and counter-ideology in the dystopias of Russian cinematography

The object of this research is a separate element of chronotope genre &amp;ndash; the time in dystopia in Russian cinematography of the late XX century &amp;ndash; early XXI centuries. The subject of this research is the ideological time &amp;ndash; a part of screenwriting that reflects the worldview of the characters through verbal constructions. Special attention is given to juxtaposition of ideology (the system proclaimed by the author) and counter-ideology (the opposing system of the public majority), as well as the manifestations of &amp;ldquo;doublethink&amp;rdquo;. Detailed analysis is conducted on the characteristic of the author's position on the ideological questions and its correlation with the social and cultural situation in the Soviet and post-Soviet society. Leaning on the theory of &amp;ldquo;vertical&amp;rdquo; time, the author explores one of its chronological layers associated with the ideology of dystopia, which defines the scientific novelty of this research. The works of K. Lopushansky, A. Sokurov, and A. German Sr. demonstrate that there can be at least to perspectives on the events, which underlies the dramaturgical conflict. The conclusion is made that various parts of ideology of the characters may be attributed to time that is different from time of the author and the audience. Another conclusion indicates the diversity of humanistic ideologies that film directors resort to, as well as different ways of their expression by the characters. The acquired results can be implemented in the genre dramaturgy, film studies, and film criticism.
A Redefinition Method of Extracting Features for Media Content Utilization and Its Application to Kimono Obi Design

In this paper, we discuss the designing of feature metadata for sensory acts, such as human perception of media data. We defined the desired properties of feature metadata in sensibility as " intelligibility", "comprehensiveness", and "orthogonality". We also define new feature metadata for Kimono Obi designs. The Obi is a kind of belt, that is wrapped around the waist over the Kimono. In addition, we propose a method to investigate the orthogonality of feature metadata among the desired properties of feature metadata. Experiments are conducted using the proposed method to evaluate the feature metadata against the design.
A New system for User Authentication Using Android Application

For most of us, Internet services have become an almost daily activity and an integral part of our lives, and these services come with many requirements regarding protection, including advanced authentication, which is usually dealt with or neglected by users. Furthermore, the passwords approach only applicable for initial logins and also doesn't have the protection against unlocked workstation attacks. In this research, an android Smartphone application with web-based presented to safeguard against security threats. The application designed primarily on the android platform features such as fingerprint as it's friendly with the clients/users and, it also unique, to make it suitable, appropriate, and sustainable. A Biometric system has been implemented as a part of a security approach for the session's management. A secure methodology is defined for perpetual authentication through continuous user verification. Finally, using biometric authentication will allow credentials to be obtained transparently without requiring user interaction or explicit notifications, which guarantee the essential usability for services.
Verification of Effectiveness of Memorizing Directions Using Virtual Reality

In recent years, technology of virtual reality (VR) has been developed, and VR has become very familiar. It is considered that the method of memorizing the content learned in a single training for a longer period of time is excellent. From this viewpoint, VR may be better than videos. In this paper, therefore, we try to clarify the effectiveness of VR in long-term memorization. We conduct an experiment to confirm the effect of VR in terms of long-term memory. The experiment focuses on the memory of directions as a task to memorize "procedures" that can be reproduced in the real world, and shows that there are significant differences in the time required for memorization and the ease of operation.
DevOps for Continuous Innovation

This chapter links the areas of IT management, DevOps, and continuous innovation. After describing some fundamentals of IT service management, the DevOps concept is presented as a new agile approach toward an integrated lifecycle of software delivery and operations based on the five guiding principles of DevOps. In particular, several established “continuous concepts” from software engineering are recognized as enablers for digitalizing development processes. As an emerging methodology, DevOps has seen various enhancements, which are also summarized in this chapter before consolidating the elements in a DevOps-based framework for continuous innovation.
Lunar Underground Mining and Ventilation Requirements

According to the Global Resources Outlook, over the past five decades, the global population has doubled, and the extraction of materials has tripled. The exponential demand for minerals and the scarcity of some essential elements on the earth would justify the need for the extraterrestrial mining besides its primary objective to help sustain the long-term space exploration needs through in-situ resource utilization. The Moon's proximity and resources such as helium-3, gold, platinum group metals, rare earth metals, and water make it a viable option. The ability to mine these valuable materials could have a tremendous impact on earth and its environment. The challenges in lunar mining include low-gravity, solar radiation, solar flares, micro-meteorites, and extreme temperature changes and their effects on mining processes. Underground mining operation seems to be more feasible to deal with issues related to extreme temperature changes and moon dust as it can wreak havoc on equipment, robots, and computers. The initial construction and mining operation will be remotely controlled from the earth using a lunar telemining operation center. Drilling and blasting will be done underground. The gases by-products of the explosives would settle in mine workings according to their density. Since it will be an entirely teleoperated mine, the ventilation system would not require much energy. However, if the geological conditions are adverse and areas that contain reactive and explosive gases need to be excavated then fume clearance/removal might be needed to protect the robots and equipment. This paper presents an overview of the lunar underground mining benefits, challenges, and ventilation requirements.
Machine Learning Based Predictive Mechanism for Internet Bandwidth

Internet of Things refers to the way that more and more physical devices are collecting and exchanging data over the internet. Internet of Things will have an increasing impact on bandwidth. Many Internet of Things devices operate wirelessly, while others are connected. Most IoT devices use less bandwidth, but many devices going online mean high bandwidth will be needed. As IoT grows, it will be necessary to have a platform which can accommodate this huge change. Due to the development of technology amount of data that is transmitted by devices is increased, which will need for increased bandwidth. For example, when smartphones start transmitting images and streaming video, need for bandwidth increases tremendously. There is no particular solution available for spectrum predictions. In this paper, we propose a machine learning prediction algorithm for internet bandwidth.
Building Block Steering Gear Robot Product Design and Optimization Direction

The main logical idea of this paper is to optimize the man-machine interaction of building block steering gear robot, to improve the man-machine interaction disadvantages of the existing products, and improve the now existing building block assembly structure, which can better meet the requirement of skeleton and appearance of steering gear robot product. It combines the Chinese mortise and tenon joint craft to improve the assembly method of building blocks, which makes the traditional robot enlightenment education more culturally meaningful. Designing and optimizing the man-machine relationship of building block steering gear robot can improve the disadvantages of the existing products and improve the appearance of the products. The optimized design can make the building block steering gear robot have more cultural features, easier to build and have more educational applications, and more beautiful appearance.
Underwater Image Enhancement for Automotive Wading

To improve the driver safety for automotive wading, the underwater image enhancement is designed to help drivers to deal with fuzzy images shown in highly scattered underwater environments and detect hidden obstacles. In addition to the traditional methods, some neural network approaches which may perform better are suggested. The purpose of this paper is to integrate traditional methods and deep learning methods to enhance underwater images. The former Deep Underwater Image Enhancement Network (DUIENET) employs a gated fusion network architecture to learn three confidence maps which will be used to combine the three input images into an enhanced result, the white balance is replaced by Multi-Scale Retinex in the modified approach. Then, some objective evaluation methods are also adopted, mean square error method and peak signal to noise ratio method, to compare the performance of the different methods. Finally, we chose some test images from test data randomly, the results show that the developed approach is better than the previous methods, the clarity of the pictures have been indeed improved.
Quartermaster: A Tool for Modeling and Simulating System Degradation

It is essential that software systems be tolerant to degradations in components they rely on. There are patterns and techniques which software engineers use to ensure their systems gracefully degrade. Despite these techniques being available in practice, tuning and configuration is hard to get right and it is expensive to explore possible changes to components and techniques in complex systems. To fill these gaps, we propose Quartermaster to model and simulate systems and fault-tolerant techniques. We anticipate that Quartermaster will be useful to further research on graceful degradation and help inform software engineers about techniques that are most appropriate for their use cases.
The Edge Feature Subtraction for Completing Video Matting

An automatic video matting method based on unsupervised matting using edge feature is proposed in this paper. Extract foreground and background objects from video sequences is an essential task as video matting. But it needs an automatic process to determine foreground and background area so the human effort can be reduced. We proposed a method to decide mechanical constraints using edge features on video matting. Edge features are used to find a gradient vector from each RGB color channel to obtain automatic scribbles. Comparing the results, alpha matte from our proposed method with ground truth has less than 0,01 in the MAE score.
Model Design and Processing of Bionic Non-smooth Surface of Fish-scale Concavity

Abstract Based on the basic methods of engineering bionics research, this paper is inspired by the desert surface morphology on the basis of the existing drag reduction microstructures, and extracts the characteristics of ecological surface drag reduction information, thereby establishing a fish-scale pit model. This paper designs and builds a diamond stamping platform to stamp and stamp copper sheets, and proposes a method that can effectively process the non-smooth surface of the fish-scale pits of the required size.
Effects of Immersion and Visual Angle on Brand Placement Effectiveness

Typical inherent properties of immersive Virtual Reality (VR) such as felt presence might have an impact on how well brand placements are remembered. In this study, we exposed participants to brand placements in four conditions of varying degrees of immersion and visual angle on the stimulus. Placements appeared either as poster or as puzzle. We measured the recall and recognition of these placements. Our study revealed that neither immersion nor the visual angle had a significant impact on memory for brand placements.
Performance counter based online pipeline bugs detection using machine learning techniques

The growing complexity of new features in multicore processors imposes significant pressure towards functional verification. Although a large amount of time and effort are spent on it, functional design bugs escape into the products and cause catastrophic effects. Hence, online design bug detection is needed to detect the functional bugs in the field. In this work, we propose a novel approach by leveraging Performance Monitoring Counters (PMC) and machine learning to detect and locate pipeline bugs in a processor. We establish the correlation between PMC events and pipeline bugs in order to extract the features to build and train machine learning models. We design and implement a synthetic bug injection framework to obtain datasets for our simulation. To evaluate the proposal, Multi2Sim simulator is used to simulate the x86 architecture model. An x86 fault model is developed to synthetically inject bugs in x86 pipeline stages. PMC event values are collected by executing the SPEC CPU2006 and MiBench benchmarks for both bug and no-bug scenarios in the x86 simulator. This training data obtained through simulation is used to build a Bug Detection Model (BDM) that detects a pipeline bug and a Bug Location Model (BLM) that locates the pipeline unit where the bug occurred. Simulation results show that both BDM and BLM provide an accuracy of 97.3% and 91.6% using Decision tree and Random forest, respectively. When compared against other state of art approaches, our solution can locate the pipeline unit where the bug occurred with a high accuracy and without using additional hardware.
PERMUTE: Response Time and Energy Aware Virtual Machine Placement for Cloud Data Centers

Cloud data centers play a significant role in providing services needed by users in a quick way. Recent studies show that, traffic patterns in data centers have a special importance to be improved, since they have significant effects on various aspects such as congestion, overall energy consumption and service response time. The traffic patterns inside a cloud data center have two categories: North-South and East-West. The former one is the outside-inside and inside-outside traffic, Whereas, the latter is the traffic among Virtual Machines (VMs) within data centers. Previous studies have shown that the East-West traffic pattern is multiple times larger than the North-South one. This leads data centers to experience congestion and packet loss in the core layer of their topology. Common cause of large traffic patterns is that, VMs of service chains are scattered within the data center in different racks, so that, it causes lots of packet injection into the data center. In this paper, we propose a heuristic algorithm to place VMs of a service chain in a closer proximity of each other to improve the East-West traffic pattern by reducing response time of services and also data centers' overall energy consumption. The simulation results compared to the state-of-the-art method demonstrate about 18% improvement in response time for users' requests and 10% of total energy consumption reduction in the data center.
Research on Unmanned Static Trajectory

Abstract With the development of science and technology, the intelligence of society is getting higher and higher. The development of safe and reliable intelligent driving has become one of the important topics in today’s era. As a high-tech complex integrating environmental awareness, planning decision-making, control execution and information interaction, driverless cars are increasingly becoming the needs of the times.[1] How to accurately extract effective information to provide safe and reliable control instructions for the next decision-making of vehicles in complex multi-source and heterogeneous environment is the technical basis of intelligent driving. In this paper, a certain amount of research has been done on lane change trajectory planning in different environments. In the aspect of static driving environment, a lane-changing trajectory planning method based on the fifth-order Bessel curve is planned. The physical problems are transformed into optimization problems of coordinate points of Bessel curve expression by planning the left and right boundaries of feasible region through lane-changing trajectory. [2]In the aspect of dynamic driving, based on the model predictive control theory, a lane-changing trajectory optimization method for multi-lane and multi-surrounding vehicles is proposed.
Monocular Visual Odometry based on joint unsupervised learning of depth and optical flow with geometric constraints

Abstract Inferring camera ego-motion from consecutive images is essential in visual odometry (VO). In this work, we present a jointly unsupervised learning system for monocular VO, consisting of single-view depth, two-view optical flow, and camera-motion estimation module. Our work mitigates the scale drift issue which can further result in a degraded performance in the long-sequence scene. We achieve this by incorporating standard epipolar geometry into the framework. Specifically, we extract correspondences over predicted optical flow and then recover ego-motion. Additionally, we obtain pseudo-ground-truth depth via triangulating 2D-2D pixel matches, which makes the depth scale is closely relevant to the pose. Experimentation on the KITTI driving dataset shows competitive performance compared to established methods.
Quasibinary Classifier for Images with Zero and Multiple Labels

The softmax and binary classifier are commonly preferred for image classification applications. However, as softmax is specifically designed for categorical classification, it assumes each image has just one class label. This limits its applicability for problems where the number of labels does not equal one, most notably zero- and multi-label problems. In these challenging settings, binary classifiers are, in theory, better suited. However, as they ignore the correlation between classes, they are not as accurate and scalable in practice. In this paper, we start from the observation that the only difference between binary and softmax classifiers is their normalization function. Specifically, while the binary classifier self-normalizes its score, the softmax classifier combines the scores from all classes before normalisation. On the basis of this observation we introduce a normalization function that is learnable, constant, and shared between classes and data points. By doing so, we arrive at a new type of binary classifier that we coin quasibinary classifier. We show in a variety of image classification settings, and on several datasets, that quasibinary classifiers are considerably better in classification settings where regular binary and softmax classifiers suffer, including zero-label and multi-label classification. What is more, we show that quasibinary classifiers yield well-calibrated probabilities allowing for direct and reliable comparisons, not only between classes but also between data points.
Numerical Analysis of Fluid Flow on Wall Cylinder Circular with K-εModeling for a High Reynolds Rate: Analisa Numerik Aliran Fluida pada Dinding Silinder Sirkular denganPemodelan K-ε untuk Bilangan Reynolds yang Tinggi

This study discusses the numerical study of two-dimensional analysis of flow through circular cylinders.The original physical information entered in the equation governing most of the modeling is transferredinto a numerical solution. Fluid flow on two-dimensional circular cylinder wall using high Reynolds k-emodeling (Re = 106), Here we will do 3 modeling first oder upwind, second order upwind and third orderMUSCL by using k-e standard. The general procedure for this research is formulated in detail for allocations in the dynamic analysis of fluid computing. The results of this study suggest that MUSCL's third order modeling gives more accurate results better than other models.
Coarse-To-Careful: Seeking Semantic-Related Knowledge for Open-Domain Commonsense Question Answering

It is prevalent to utilize external knowledge to help machine answer questions that need background commonsense, which faces a problem that unlimited knowledge will transmit noisy and misleading information. Towards the issue of introducing related knowledge, we propose a semantic-driven knowledge-aware QA framework, which controls the knowledge injection in a coarse-to-careful fashion. We devise a tailoring strategy to filter extracted knowledge under monitoring of the coarse semantic of question on the knowledge extraction stage. And we develop a semantic-aware knowledge fetching module that engages structural knowledge information and fuses proper knowledge according to the careful semantic of questions in a hierarchical way. Experiments demonstrate that the proposed approach promotes the performance on the CommonsenseQA dataset comparing with strong baselines.
Tagbly: Enhanced Multimedia

Tagbly is an acronym for Text Audio Graphics Assem BLY. It is a method and system that synchronizes audio data with image data using an HTML control file. The HTML control file contains references to linked multimedia asset files and timestamps indicating when each of these multimedia assets will be displayed/played. Audiovisual content creators can incorporate interactive content, such as graphic elements (e.g., pictures, 360 images and 3D models), text-based annotations, clickable links and buttons, and zooming in/out of images. Viewers can interact with the interactive features while the multimedia content is being displayed/played. In the viewer's eye, Tagbly's audiovisual content looks like an “interactive video”.