Reviewerly/exHarmony huggingface dataset:
Mineral resource database for deposits related to the Mesoproterozoic Midcontinent Rift System, United States and Canada

First posted August 3, 2020 For additional information, contact: Coordinator, Mineral Resources ProgramU.S. Geological Survey913 National CenterReston, VA 20192 The Midcontinent Rift System (MRS) of North America is one of the world’s largest continental rifts and has an age of 1.1 Ga (giga-annum). The MRS hosts a diverse suite of magmatic and hydrothermal mineral deposits in the Lake Superior region where rift rocks are exposed at or near the surface. As part of the construction of a database summarizing information on mineral deposits in the MRS, data from regional mineral deposits were downloaded from the U.S. Geological Survey (USGS) Mineral Resources Data System (MRDS), the USGS Mineral Deposit Database (USMIN), and the Ontario Ministry of Energy, Northern Development and Mines Mineral Deposit Inventory (MDI). Deposits related to MRS rocks or mineralizing events were identified and compiled into a database to develop a space/time classification for MRS-related mineral deposits. Information from MRDS, USMIN, and MDI records and from the extensive literature describing MRS mineral deposits was used to classify each entry by deposit type, host rock age and type, and estimated mineralization age. Most deposits were readily classified because of unique mineralogy, location, or well-constrained host rock. These deposits were then put into a tectonic evolutionary framework for the MRS, which showed that many deposits formed within discrete spatial and temporal stages of rift evolution.
Future-Frame Prediction for Fast-Moving Objects with Motion Blur

We propose a deep neural network model that recognizes the position and velocity of a fast-moving object in a video sequence and predicts the object’s future motion. When filming a fast-moving subject using a regular camera rather than a super-high-speed camera, there is often severe motion blur, making it difficult to recognize the exact location and speed of the object in the video. Additionally, because the fast moving object usually moves rapidly out of the camera’s field of view, the number of captured frames used as input for future-motion predictions should be minimized. Our model can capture a short video sequence of two frames with a high-speed moving object as input, use motion blur as additional information to recognize the position and velocity of the object, and predict the video frame containing the future motion of the object. Experiments show that our model has significantly better performance than existing future-frame prediction models in determining the future position and velocity of an object in two physical scenarios where a fast-moving two-dimensional object appears.
Psychological-pedagogical support of professional training of future educators of preschool education institutions

Науковий часопис НПУ імені М. П. Драгоманова individual approach, consistency and complication of material, clarity); the basic directions of the music means using during correction and development work (listening to music, singing, musical-rhythmic movements, playing on children's musical instruments, etc.); tasks that children are offered to perform while listening to music (for the development of auditory attention and memory and auditory control), singing (as a complex process of sound formation to correct disorders of articulation, breathing, sound, diction), playing musical instruments (in order to develop a sense of rhythm, breathing).However, music exercises aimed at the development of auditory perception and phonemic processes, fixing the correct pronunciation of sounds of preschool children with speech disorders are presented in the article.
Cognitive Aspects Of Fraktal Modelling

This article deals with stylistic convergence study in the aspects of cognitive linguistics and linguasynergetics.The object of the research is fraktal modelling of textual spaces of stylistic convergence.The purpose of the study is to consider fraktal modelling of the textual space of stylistic convergence as a cognitive process.Fraktal models demonstrate sense development in converged textual spaces under the influence of the semantic attractor.Those senses represent cognitive features of certain concepts actualized by stylistic devices.The purpose of the study has determined the research methods, including the method of stylistic study (in revealing stylistic devices in the process of visual perception); the hermeneutical study method (in interpreting senses actualized by stylistic devices); the method of cognitive analysis (to state sense relations in converged textual spaces and to define the structure-forming principles of interconnection, opposition, development); the method of fraktal modelling (to reveal and to define the configuration of semantic textual space).The fraktal models "spiral", "letter X"/ "figure 8", "enclosed 8 figures" demonstrating the sense development in the converged textual spaces are described in the work.The material of the research includes textual fragments of I. Stone's works Sailor on Horseback and They Also Ran.Fraktal modelling gives a vivid picture of the concept represented by semantic fraktals actualized in the textual spaces of stylistic convergence.The process of fraktal modelling uses the cognitive mechanisms of profiling and topology as well as mental operations of understanding, solving some problems, thinking, perception, comprehension and others.
Probabilistic Machine Learning Using Bayesian Inference

Machine Learning is a branch of AI (Artificial Intelligence) which expands on the idea of a computational system extending its knowledge about set methodical behaviors from the data that is fed to it to essentially develop analytical skills that can help in identifying patterns and making decisions with little to no participation of a real human being. Computer algorithms help in gaining experience to improve the facility over time for use by both consumers and corporations. In today’s technologically advanced world, Machine Learning has given us self-driving cars, speech recognition software, and AI agents like Siri and Google assistant. This project evaluates how the Beta function came to be and how Stirling’s formula is implemented in calculating the magnitude of this function for large input values. The Beta function can then be used to produce a Beta distribution of probabilities to find whether people will actually watch a video they come across on their recommendations feed or search feed and then using Bayesian inference update the prior set predictions.
Security Threats Posed by Contraband Cell Phones in Prisons: Prison Officers’ Perspectives

In the recent past, cell phones have emerged to be among the leading outlawed contemporary contrabands in correctional facilities around the globe. Although official statistics on the number of contraband cell phones globally remains elusive, searches by prison officials project a worrisome trend. At Naivasha Maximum Prison, it is estimated that 30% of the inmates possess one cell phone. Existing studies are more inclined to how phones are smuggled, who smuggle them and their impact on rehabilitation of offenders. Limited studies have been carried out to examine the security threats posed by smuggling and use of cell phones into the prisons. One school of thought argues that cell phones are smuggled into prisons and used by inmates for a good course including communicating with their significant others and for leisure. The other school of thought argues that they are used to commit crimes and thus a source of insecurity to officers, inmates and the members of public. This paper is guided by three specific objectives; to examine security threats prison officers are prone to due to smuggling and use of cell phones at the prison; to examine security threats inmates are prone to due to smuggling and use of cell phones at the prison and finally to examine security threats members of public are prone to due to smuggling and use of cell phones at the prison. The study employed descriptive research design. The unit of analysis was an individual prison officer. A sample size of 182 respondents was drawn from a population of 507 junior prison officers using simple random sampling technique. Primary data was collected through questionnaires. The results of this study indicated that prison officers are prone to physical injuries (38.92%), ambushes during escorts (29.94%) and espionage (20.35%). Inmates were prone to physical attacks (46.78%), electronic fraud (26.28%) and exploitation (21.79%). Members of public were prone to electronic fraud (74.40%) and kidnappings (40.47%). This current study concluded that cell phones in prison facilities pose significant security threats to prison officers, inmates and the members of public. These security threats are therefore of national concern. This study will add to existing body of knowledge on prisons and address policy gaps that exists in controlling cell phones in prison facilities.
It’s Anti-Suit Injunctions All The Way Down – The Strange New Realities of International Litigation Over Standards-Essential Patents

Today’s markets for technology products — from smartphones to home appliances to automobiles — are inherently global. This is especially true of products that embody technical standards — protocols like 5G, Wi-Fi, Bluetooth and USB that are covered by hundreds, thousands, or tens of thousands of patents (so-called “standards-essential patents” or “SEPs”). Given the global scope and size of these markets, it is not surprising that patent litigation over standardized products is often conducted on a global scale. This article looks at an increasingly important aspect of these global standards wars: the ability of a court in one jurisdiction to prevent a party from pursuing litigation in another jurisdiction using a procedural mechanism called the anti-suit injunction (ASI). To complicate matters further, a litigant may also petition a court in one jurisdiction to prevent a party from seeking an ASI in another jurisdiction — the so-called anti-anti-suit injunction (AASI). And, curiouser still, litigants have recently re-invigorated the anti-anti-anti-suit injunction (AAASI), a procedural move that seeks to prevent a litigant from obtaining an AASI to block another litigant from requesting an ASI. If there is no theoretical limit to the procedural machinations to which parties can go in such disputes, it may, indeed, be injunctions “all the way down”.
TRAFFIC SIGN DETECTION USING DEEP LEARNING

Convolutional Neural Networks mostly use deep learning algorithms to detect and identify traffic signs till now but they are lacking in so many ways.This paper will give a really effective method for traffic sign detection and identification using convolutional neural networks.Convolutional Neural Networks are used for road sign detection and classification as it takes an input image and then assigns weights to different aspects in the image and then differentiate them from each other.Other classification algorithms require much longer preprocessing than the ConvNet.The filters which are there in primitive methods are engineered manually with training.These filters are learned by the ConvNets.Neurons respond to stimuli in the receptive field only, which is a restricted region of the visual field.The temporal and spatial dependencies of an image can be successfully captured by applying the relevant filters.To understand the sophistication of an image in a better way, the network can be trained.After reducing the parameters and weights reusability, the architecture fits better with the image dataset.The architecture of the system is designed in such a way that it extracts important features from the traffic sign's images and classifies them under various categories.
Cross-Camera Erased Feature Learning for Unsupervised Person Re-Identification

Most supervised person re-identification methods show their excellent performance, but using labeled datasets is very expensive, which limits its application in practical scenarios. To solve the scalability problem, we propose a Cross-camera Erased Feature Learning (CEFL) framework for unsupervised person re-identification that learns discriminative features from image appearances without manual annotations, where both of the cross-camera global image appearance and the local details are explored. Specifically, for the global appearance, in order to bridge the gap between images with the same identities under different cameras, we generate style-transferred images. The network is trained to classify the original images, the style-transferred images and the negative samples. To learn the partial details of the images, we generate erased images and train the network to pull the similar erased images together and push the dissimilar ones away. In addition, we joint learn the discriminative global and local information to learn a more robust model. Global and erased features are used together in feature learning which are successful conjunction of BFENet. A large number of experiments show the superiority of CEFL in unsupervised pedestrian re-identification.
A COMPARATIVE STUDY OF FREQUENT ITEMSET MINING ALGORITHMS

Data mining has a great role for making market revenue by its decisive quality. Frequent Itemsets Mining (FIM) is very focused and emerging field of research of data mining. .FIM is used in association rule mining and other data analytic tasks. Frequent itemsets are patterns those occur very frequently in dataset. This paper provides efficacious and in-depth study of FIM algorithms. A comparative study of existing FIM algorithms and their variations are analyzed to find the limitations of these algorithms. Apriori ,Fp Growth, Eclat are basic methodologies for Frequent itemset mining. But there is issue for efficiency and memory consumption. Initially FIM was started in 1990s and first algorithm, Apriori has drawback of scanning database multiple time, so complexity and memory consumption was high. Then Fp-Growth algorithm required scanning of dataset twice to form Fp-tree so huge memory consumption was required to hold the fp tree for Frequent Itemset Mining. After it Parallel frequent mining (shared and non-shared memory) overcome the drawback of FP-Growth algorithms by balancing the workload of single node to achieve efficiency. Now in present era of big dataset with voluminous and growing data at every moment, more prominent, efficient and scalable algorithm is required as complexity of these algorithms exponentially increases with the growth of rapid data .So objective of the study is in-depth literature review of Frequent Itemsets Mining algorithms to develop an efficient FIM algorithm.
Mass: Workload-Aware Storage Policy for OpenStack Swift

A cloud object store entails serving workloads of multi tenants. Different applications have different access characteristics and various performance requirements. Thus, it is necessary for the cloud object store to provide tenant-specific policies. OpenStack Swift develops a storage policy mechanism to enable separate storage configurations for different applications. However, the limited configurability of the policy mechanism makes it difficult to provide efficient and flexible policies to meet the evolving needs of the applications. First, original policies only control the frontend paths of the request forwarding without covering the backend paths of the request processing. Thus, they cannot provide sufficient optimizations for workload performance. Second, those policies need to be pre-defined and remain unchanged at runtime, so that they lack the flexibility to adapt to the possible workload changes during runtime. In addition, effective execution of tenant-specific policies is based on the premise that all storage layers are aware of the requests’ sources, while the storage server layer lacks the requisite information to distinguish requests from multi tenants.
Empathy as a Response to Frustration in Password Choice

Previous research often reports that password-based security is frustrating, irritating or annoying, and as a result it often leads to weak password choices. We investigated the impact of empathy as a countermeasure to the anger-related states. We designed an online study with N = 194 participants. The experimental group received an empathic message while the control group did not. Participants presented with the empathic message created significantly stronger passwords than those who did not receive the message. Our finding differs from previous research because it shows participants creating stronger passwords with an empathic response to anger arousal. This antidote to frustrated states with regards to password choice provides an initial step towards more supportive and emotionally intelligent security designs.
Humanitarization of Technical Education: Challenges and Modern Approaches

The article is devoted to the analysis of the role of humanitarization of technical education in resolving issues of technogenic civilization.The relevance of this topic is based on the complexity and global nature of the problems that can be solved by the whole humanity or by each individual independently.Within the framework of technogenic civilization, many aspects of relations between society and nature, man and society, and the interactions of various human communities are particularly acute as issues of the survival of mankind.Of particular danger in the modern era is terrorism, the aggravating environmental situation, demographic, raw materials and other problems.The article proves that all that we call "humanitarization of education", including personal orientation of education, mainstreaming of the values of human life, the use of personality potential, and culture centricity, can qualitatively change attitudes towards the world and people and provide an optimal solution to the problems of technogenic civilization.The presented study implements an integrated approach that allows us to use historical and cultural, and topic addressed methods to determine the factors, tasks directions and expected results of the humanitarization of education.The author analyzes the educational process of the famous Russian technical university, Moscow State University of Civil Engineering, as an example of the successful humanitarization of technical education.The humanitarian aspects of education at this university include: 1) the close relation between science and education, the persistent desire to provide on this basis effective research and educational activities aimed at improving the quality of educational services; 2) fundamental and applied social research on a wide range of priority areas for the development of science and technology; 3) historical, philosophical and cultural analysis of the problems of construction and architecture; 4) a highly effective system of education in the humanities for masters and qualified specialists; 5) training of students on the best traditions of education in the field of construction and the industry as a whole, etc.The culture-centric approach is defined as a modern approach to the humanitarization of technical education, which is associated with the search for the axiological foundations of modern education and the determination of its basic humanistic values.
A Hybrid Fuzzy Time Series Technique for Forecasting Univariate Data

In this paper a hybrid forecasting technique that integrates Cat Swarm optimization Clustering (CSO-C) and Particle Swarm Optimization (PSO) with Fuzzy Time Series (FTS) forecasting is presented.In the three stages of FTS, CSO-C found application at the fuzzification module where its efficient capability in terms of data classification was utilized to neutrally divide the universe of discourse into unequal parts.Then, disambiguated fuzzy relationships were obtained using Fuzzy Set Group (FSG).In the final stage, PSO was adopted for optimization; by tuning weights assigned to fuzzy sets in a rule.This rule is a fuzzy logical relationship induced from FSG.The forecasting results showed that the proposed method outperformed other existing methods; using RMSE and MAPE as performance metrics.
DUAL PYRAMIDS ENCODER-DECODER NETWORK FOR SEMANTIC SEGMENTATION IN GROUND AND AERIAL VIEW IMAGES

Abstract. Semantic segmentation is a fundamental research task in computer vision, which intends to assign a certain category to every pixel. Currently, most existing methods only utilize the deepest feature map for decoding, while high-level features get inevitably lost during the procedure of down-sampling. In the decoder section, transposed convolution or bilinear interpolation was widely used to restore the size of the encoded feature map; however, few optimizations are applied during up-sampling process which is detrimental to the performance for grouping and classification. In this work, we proposed a dual pyramids encoder-decoder deep neural network (DPEDNet) to tackle the above issues. The first pyramid integrated and encoded multi-resolution features through sequentially stacked merging, and the second pyramid decoded the features through dense atrous convolution with chained up-sampling. Without post-processing and multi-scale testing, the proposed network has achieved state-of-the-art performances on two challenging benchmark image datasets for both ground and aerial view scenes.
VEHICLE TRACKING AND SPEED ESTIMATION FROM UNMANNED AERIAL VIDEOS

Abstract. In this paper, a solution for vehicle speed estimation using unmanned aerial videos is described. First, convolutional neural networks and Kalman filtering using deep features are used for detecting and tracking vehicles. Then, a photogrammetric approach is developed for estimating the three-dimensional (3D) position of the tracked vehicles on the road, which allows determining their speed. No assumptions are made about either the 3D structure of the road (e.g., constraining it to be a planar surface) or the camera pose (e.g., restricting it to be stationary). Therefore, this solution applies to videos acquired by a moving unmanned aerial vehicle from complex road structures (e.g., multi-level highways). This solution is also robust to changes of viewpoint and scale, which makes it applicable to situations where cars undergo orientation and resolution changes as observed from the sky (e.g., in roundabouts). Experiments showed that a high detection accuracy could be achieved with an F1-score of 94.54%. Besides, the tracking technique performed well, with a multiple-object tracking accuracy of 89.8% at a speed of 11 frames per second on videos of 2720×1530 pixels. Vehicle positioning (and thus, speed estimation) could be performed with an average accuracy of 0.6 m.
MINDFLOW BASED DENSE MATCHING BETWEEN TIR AND RGB IMAGES

Abstract. Image registration is a fundamental issue in photogrammetry and remote sensing, which targets to find the alignment between different images. Recently, registration of images from difference sensors become the hot topic. The registered images from different sensors are able to offer additional information, which help with different tasks like segmentation, classification, and even emergency analysis. In this paper, we proposed a registration strategy to calculate the dominant orientation difference and then achieve the dense alignment of Thermal Infrared (TIR) image and RGB image with MINDflow. Firstly, the orientation difference of TIR images and RGB images is calculated by finding the dominant image orientations based on phase congruency. Then, the modality independent neighborhood descriptor (MIND) together with global optical flow algorithm are adopted as MINDflow for dense matching. Our method is tested in the image sets containing TIR images and RGB images captured separately but in the same construction site areas. The results show that it is able to achieve the optimal results with features of significance even for dramatically radiometric differences between TIR images and RGB images. By comparing the results with other descriptor, our method is more robust and keep the features of objects in the images.
Cognitive model in the structure of pedagogical technology

Н. В. Ярославцева, А