Reviewerly/exHarmony huggingface dataset:
Visual Attention: Deep Rare Features

Human visual system is modeled in engineering field providing feature-engineered methods which detect contrasted/surprising/unusual data into images. This data is “in-teresting” for humans and leads to numerous applications. Deep learning (DNNs) drastically improved the algorithms efficiency on the main benchmark datasets. However, DNN-based models are counter-intuitive: surprising or unusual data is by definition difficult to learn because of its low occurrence probability. In reality, DNNs models mainly learn top-down features such as faces, text, people, or animals which usually attract human attention, but they have low efficiency in extracting surprising or unusual data in the images. In this paper, we propose a model called DeepRare2019 (DR) which uses the power of DNNs feature extraction and the genericity of feature-engineered algorithms. DR 1) does not need any training, 2) it takes less than a second per image on CPU only and 3) our tests on three very different eye-tracking datasets show that DR is generic and is always in the top-3 models on all datasets and metrics while no other model exhibits such a regularity and genericity. DeepRare2019 code can be found at https://gilhub.com/numediart/VisualAttention-RareFamily. Contribution-DeepRare2019 (DR) uses the power of DNNs feature extraction and the genericity of feature-engineered algorithms to provide accurate visual attention prediction in any situation.
Satellite data platform LandBrowser provided by AIST

This paper introduces Landsat-8/ASTER/Sentinel-2 unified data global graphic user interface (GUI) developed by AIST. Users can freely browse, search, and download each band and true color image of global Landsat-8 data, global ASTER data, and true color image of global Sentinel-2 data.
Optimization of Cardiac Magnetic Resonance Synthetic Image Based on Simulated Generative Adversarial Network

The generative adversarial network (GAN) has advantage to fit data distribution, so it can achieve data augmentation by fitting the real distribution and synthesizing additional training data. In this way, the deep convolution model can also be well trained in the case of using a small sample medical image data set. However, some certain gaps still exist between synthetic images and real images. In order to further narrow those gaps, this paper proposed a method that applies SimGAN on cardiac magnetic resonance synthetic image optimization task. Meanwhile, the improved residual structure is used to deepen the network structure to improve the performance of the optimizer. Lastly, the experiments will show the good result of our data augmentation method based on GAN.
Deep Learning Methods and Their Application to Nursing Workflows

Mahajan, Amey BS, BA; Mahajan, Satish M. PhD, MStat, MEng, RNEditor(s): Alexander, Susan DNP, ANP-BC, ADM-BC Author Information
Criminalizing Virtual Theft: A Dutch Approach

Abstract: the authors aim at the assessment the possibility of considering virtual objects as objects of virtual theft. According to the Supreme Court of the Netherlands practice and the Dutch Penal Code 1881, attacks on virtual objects may be prohibited by criminal law. The authors come to the conclusion that despite the specific subject of the act, criminal liability for the offense should be established. The authors propose a version of improving the Russian approach to the qualification of such acts.
Entropy-based Recognition of Anomalous Answers for Efficient Grading of Short Answers with an Evolutionary Clustering Algorithm

Short answer question is a common assessment tool eliciting a specific textual response for knowledge assessment. The divide-and-grade is an automated grading approach that uses clustering to assign answers into sets each of which is supposed to be sufficiently similar to receive the same grade. This approach can potentially produce accurate grading with significantly reduced manual grading effort. Many current clustering methods are known to suffer from the presence of anomalies, answers that are deviated from the modes of model answers or common misconceptions. The freedom afforded to the composition of answers often lead to these anomalies, and in particular, contextual anomalous answers that are marginally correct or wrong. This paper proposes an evolutionary clustering method for the divide-and-conquer approach. The method is coupled with anomalous answer recognition based on an entropy formulation of the cluster membership of answers for rectifying misclustered answers. The method has been evaluated with an open short answer grading dataset and the accuracy compares favorably with existing algorithms. Result analysis has suggested that the method can effectively leverage small manual grading effort on issues of great impact on short answer grading accuracy.
Trace Element Composition of Sulfides from Kayad Zn-Pb deposit, Ajmer, Western India: Implications for Ore Mineralization

Earth and Space Science Open Archive posterOpen AccessYou are viewing an older version [v1]Go to new versionTrace Element Composition of Sulfides from Kayad Zn-Pb deposit, Ajmer, Western India: Implications for Ore MineralizationAuthorsEileenaDasiDSee all authors Eileena DasiDCorresponding Author• Submitting AuthorJadavpur UniversityiDhttps://orcid.org/0000-0002-8556-9778view email addressThe email was not providedcopy email address
“A STUDY TO ASSESS THE EFFECTIVENESS OF VIDEO ASSISTED TEACHING WITH REINFORCEMENT HANDBOOK ON KNOWLEDGE REGARDING SELF-CARE MANAGEMENT AMONG HEMODIALYSIS PATIENTS AND ALSO ON SELF-CARE BEHAVIOR OF PATIENTS IN SHRI VINOBA BHAVE CIVIL HOSPITAL, DADRA &amp; NAGAR HAVELI.”

Background: One of the greatest challenges that will face by the health system globally in the 21stcentury will be the increasing burden of chronic kidney diseases. Self-care management becomes an inevitable and significant task for them. Aim: The aim of the study was to assess the effectiveness of VAT with reinforcement handbook on knowledge patient and their self-care behavior among hemodialysis. Methods: An evaluative approach with one group pretest post -test design was adopted for the study. The study was conducted in SVBCH, Silvassa among 70 hemodialysis patients using convenience sampling method. Results: The results of the study revealed that the posttest mean knowledge scores of patient(18.6), and the self-care behavior of patients(111.5) found higher that the pretest mean(13.1, and 103.6) scores and the obtained ‘t’ values (patient- 11.02, patient’s self-care behavior-7.99, were greater than the table value 1.995 and it found to be highly significant at p&lt;0.05. Hence the intervention was effective in improving knowledge and self-care behavior. The knowledge scores and the self-care behavior scores of hemodialysis patients found positively correlated (0.3). There was a significant association between hemodialysis patient pretest knowledge &amp; educational status. There was no significant association between patient pretest self-care behavior &amp; selected demographic variables. Conclusion: The study results confirmed that the knowledge regarding self-care management of hemodialysis patient significantly increased among patients after administration of video assisted teaching with reinforcement handbook. Self-care behavior also increased among patients along with the knowledge of patients.
Usability Evaluation over Multiplayer Games on Display Wall Systems

This paper evaluates a usability study comparing different proposed controllers (Web, Accelerometer and Keyboard) of multiplayer games to discover which are the best methods to interact with display walls. A set of tasks were designed to test user experience and get feedback data from 40 participants. The results indicate that although the keyboard has best results on satisfaction compared to mobile controllers, it has scalability limitations, consequently further research is opened to improve the mobile controllers to achieve a more scalable possibility of players.
Visual Servo Control of Industrial Robot Based on Convolutional Neural Network

Abstract Aiming at the problem of poor flexibility of traditional industrial robot, the vision system and industrial robot control system are combined to propose a visual servo control method based on Faster RCNN. The image information of the target object is collected through the camera, and the Convolutional Neural Network(CNN) is used to detect the entire picture, then the category and position of the object to be grasped are obtained, which improves the control performance of the robot system. Experiments show that the visual servo robot control system can quickly complete the position recognition of different objects. Mean average precision of this method can reach 0.867. The maximum error between the recognized grasping position and the actual position on the coordinate axis does not exceed 3.4 mm, the detection time of each picture in the GPU environment is 17.1ms, which has certain guiding significance in the visual servo control of industrial robots.
Secure Search and Storage Services in Cloud and Fog/Edge Computing

As an extension of cloud computing, fog/edge computing migrates enormous computing and storage resources to the network edge, which forms an edge layer that is close to end devices. However, security concerns hinder the adoption of fog/edge computing. This book chapter researches the problem of secure storage and search services in fog/edge computing. From the aspect of storage security, we first propose a hierarchical-attribute-based encryption (HABE) scheme to efficiently achieve fine-grained access control in clouds, and then we combine HABE and Proxy Re-Encryption (PRE) by incorporating time concept into PRE to achieve user revocation automatically. From the aspect of search privacy, we propose a dynamic attribute-based keyword search (DABKS) scheme to achieve fine-grained search authorization in cloud computing. DABKS delegates the policy updating operations to the cloud, by incorporating proxy re-encryption (PRE) and a secret sharing scheme (SSS) into attribute-based keyword search (ABKS). The research results will play an important role in attribute-based encryption and searchable encryption, which could help create new situations for providing secure services in cloud computing and fog/edge computing.
Spectral Learning of Semantic Units in a Sentence Pair to Evaluate Semantic Textual Similarity

Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two snippets of text. It has applicability in a variety of Natural Language Processing (NLP) tasks. Due to the wide application range of STS in many fields, there is a constant demand for new methods as well as improvement in current methods. A surge of unsupervised and supervised systems has been proposed in this field but they pose a limitation in terms of scale. The restraints are caused either by the complex, non-linear sophisticated supervised learning models or by unsupervised learning models that employ a lexical database for word alignment. The model proposed here provides a spectral learning-based approach that is linear, scale-invariant, scalable, and fairly simple. The work focuses on finding semantic similarity by identifying semantic components from both the sentences that maximize the correlation amongst the sentence pair. We introduce an approach based on Canonical Correlation Analysis (CCA), using cosine similarity and Word Mover’s Distance (WMD) as a calculation metric. The model performs at par with sophisticated supervised techniques such as LSTM and BiLSTM and adds a layer of semantic components that can contribute vividly to NLP tasks.
Automatic annotation of pedestrians in thermal images using background/foreground segmentation for training deep neural networks

Deep Neural Networks for object detection have become of significant interest with the substantial improvement in their efficiency and increase in their applications. However, training such a network requires a large annotated dataset, which is very expensive in term of time and human effort. In this context, several automatic image annotation solutions have been proposed which are dependent on rich visual features and thus suitable for color images only. On the other hand, recent experiments have proven that color cameras alone are not enough for pedestrian related applications and there is a need to use other visual sensors as well, such as thermal cameras. In this paper, we propose an automatic image annotation technique for pedestrian detection in thermal images using an adaptive background/foreground estimation model to train Faster-RCNN. The results presented in this paper, obtained through longterm experiments demonstrate the efficacy of our technique. The results also show that our proposed technique is be very useful to generate image annotations automatically and to train a deep neural network without having a manually annotated dataset for cameras with different modalities.
pyMMAX2: Deep Access to MMAX2 Projects from Python

pyMMAX2 is an API for processing MMAX2 stand-off annotation data in Python. It provides a lightweight basis for the development of code which opens up the Java- and XML-based ecosystem of MMAX2 for more recent, Python-based NLP and data science methods. While pyMMAX2 is pure Python, and most functionality is implemented from scratch, the API re-uses the complex implementation of the essential business logic for MMAX2 annotation schemes by interfacing with the original MMAX2 Java libraries. pyMMAX2 is available for download at http://github.com/nlpAThits/pyMMAX2.
Characterizing Pathogenic Social Media Accounts

Over the past years, political events and public opinion on the Web have been allegedly manipulated by “Pathogenic Social Media (PSM)” accounts dedicated to spreading disinformation and performing malicious activities. These accounts are often controlled by terrorist supporters, water armies, or fake news writers and hence can pose threats to social media and general public. Understanding and analyzing PSMs could help social media devise sophisticated techniques to stop them from reaching their audience and consequently reduce their threat. In this chapter, probabilistic causal inference and well-known statistical technique Hawkes processes are utilized to distinguish between PSM and non-PSM accounts. Results on real-world ISIS-related datasets from Twitter demonstrate that PSMs behave significantly differently from regular users while disseminating information.
Influence of Presentation Mode on User's Mental Map in Temporal Sequence Data Visualization

Spatial aggregation and small multiples are two mainstream visualization presentation methods for temporal sequence data. This study compares the effect of spatial aggregated and small multiples in mental map construction with three task genres: peak value identification (Task 1), variation tracking (Task 2), and trend comparison (Task 3). Spatially aggregated timestamps get transited through animations. Task completion time favors small multiples in Task 1, but spatial aggregation with animated transition is more favorable in Task 2 and Task 3 in terms of completion time and error rate. Subjective rating results are aligned with behavioral performance measures. This research can guide future temporal sequence data visualization design.
Design and Implementation of High Performance Elliptic Curve Coprocessor Based on Dual Finite Field

In order to realize the scalar multiplication algorithm in GF (p) and GF (2m), and reducing the module implementation area, a high performance elliptic curve cryptographic coprocessor supporting dual finite field is designed and implemented. By multiplexing the shift register, the operation efficiency of the modular multiplication algorithm is greatly improved. On the other hand, combining multiplication and inverse operations, which reduces the extra modular multiplication operation after inverse operation and reduces the complexity of implementation. After verification by comparing SYSTEMC model with RTL coding, the function of the improved algorithm is correct. The result of hardware simulation shows that the equivalent unit gate is 27092 by 0.13 um SMIC standard cell process library. The modified modular multiplication requires 37.2 us, and the modular inverse requires only 96.1 us under 50 MHz clock constraint, average of 233 bit operation in GF (2m) and 256bit operation in GF (p). Compared with previous works, it has a large advantage in terms of area, and the speed has reached the expected goal.
Design and implementation of web page classification tool based on word vector

With the continuous development of the Internet, the number of web pages is growing rapidly. How to find the webpage information people need in a total number of web pages has become a difficult challenge. In many web pages, manual classification is obviously unrealistic. In view of the difficulty in obtaining tools or software that can be used for automatic classification of web pages [1,11,12,13] on the Internet, this topic designs and implements a web page classification tool based on word vector. With this tool, we can classify a large number of uncategorized pages based on some of the pages that have been classified.
Ray tracing collision detection based on GPU pipeline reorganization

Abstract This paper proposes a parallel collision detection algorithm based on ray tracing to improve GPU performance. The algorithm uses parallel ray tracing pipeline technology to improve the performance of the graphics processing unit (GPU). When the GPU combines multiple ray tracing, it will cause the work group threads to diverge, resulting in too many idle threads, which will reduce the performance of the system. In order to avoid thread divergence, a new collision detection technology for the detailed detection stage of ray tracing using a pipeline structure is proposed. By optimizing the addition of buffers, the ray tracing is divided into three stages, and the pipeline is designed to integrate multiple rays tracking to avoid unnecessary synchronization issues between the CPU and GPU, thereby improving system performance. The comparison experiment results show that the proposed algorithm improves the system performance by 2.7 times.
Corporate Cybersecurity Strategy to Enable Artificial Intelligence and Internet of Things

You are under attack … and you do not know it. The truth is, most organizations are breached within 24 hours, and attackers lurk in the environment for more than a year before they are detected. Little wonder that by 2022 the cybersecurity industry is expected to gross more than $232 billion globally and that demand for cybersecurity professionals is expected to rise to six million worldwide by 2019. This chapter will address why in the face of all those statistics we do not act and when we do, why we do not do enough. The chapter will offer some new lenses through which executives can view cybersecurity to overcome that inertia we all feel. In addition, provide some thoughts on what steps you can take to protect your companies in the short and long term alike, to build a little momentum, so you do not have to wait until your company is under attack to act. Because at the end of the day, the companies that win in the decades to come will be the ones that not only take action on cybersecurity but make it part of their corporate strategy.
Adaptive Primary Speech Signals Codecs for Software-Configured Radio Systems

Algorithms for software-configured adapting primary codecs to acoustic noise are considered, as well as adaptation under the influence of interference in communication channel in order to ensure the optimal ratio of restored speech quality at minimum transmission speed based on the theorem of V.A. Kotelnikov and Khurgin-Yakovlev representation. The analysis of primary codecs providing the best quality of restored speech at encoder output under the action of various types of acoustic noise is carried out. It is shown that the use of Khurgin-Yakovlev representation provides additional gain in the quality of reconstructed speech signal by 0.1 ... 0.5 points, according to GOST R 50840-95 with separate coding of signal samples and the first derivative, simpler implementation of synthesizing filters, as well as the possibility of parallel processing. An algorithm for adapting Khurgin-Yakovlev in the structure of adaptive codec is proposed. The application of this algorithm allows increasing the quality of restored speech by 1 ... 1.5 points according to GOST R 50840-95 under the action of acoustic noise in comparison to similar non-adaptive codecs based on the theorem of V.A. Kotelnikov or the possibility of reducing transmission speed by 15 ... 30% for software-configured radio systems to receive, transmit and process information.
Motivational and Value-Based Orientation of Pre-Conscription Youth toward Military Service

Due to the collapse of the Soviet system of military professional orientation it has become necessary to review the work with pre-conscription youth oriented toward military service; the work, includes, among other things, expanding a network of cadet educational organizations. The main goals in forming the military-professional orientation of pre-conscription youth are achieved by providing resources for the educational process, service and extracurricular activities. Motivational and value-based attitude toward military service is developed throughout the entire educational period; this contributes to the formation of cadets’ readiness to become career military officers and choose military service as a priority type of professional activity. Having analyzed psychological and pedagogical literature, we came to the conclusion that the system of military orientation of pre-conscription youth currently implemented in cadet corps, will contribute to the effective development of young people’s orientation toward military service. Key words: military service, motivational and value-based orientation, pre-conscription youth, cadets, cadet corps.
Blade Displacement Measurements of a Rotor in Forward Flight in the Langley 14- by 22-Ft Wind Tunnel

Stereo photogrammetry was used to measure the elastic bending and twist of a three-bladed rotor in forward flight in the NASA Langley 14- by 22-Ft Subsonic Wind Tunnel. The rotor was imaged from below by two cameras mounted at windows of a large turntable in the floor of the test section. At each test condition, the turntable and cameras were rotated and the cameras were triggered to allow measurements over a range of blade azimuth angles. Retro-reflective targets were applied along the leading and trailing edges of the lower surface of each blade. Image blur due to blade motion was minimized by illuminating the targets with high-intensity LED flash lamps mounted next to each camera. The cameras were re-calibrated at each angular position of the turntable using the positions in each image of retro-reflective targets on the ceiling of the test section whose spatial coordinates had been very accurately measured. The blade displacement measurements yielded elastic bending and twist along each blade as functions of blade azimuth, thrust coefficient, and advance ratio, all at a constant rotor shaft angle. In addition, the measurements produced estimates of the rigid-body blade pitch, flap, and lag angles.