Reviewerly/exHarmony huggingface dataset:
DeepAttent: Saliency Prediction with Deep Multi-scale Residual Network

PredictingDwivedi, Kshitij where humans look in a given sceneSingh, Nitin is a well-known problemShanmugham, Sabari R. with multiple applicationsKumar, Manoj in consumer cameras, human–computer interaction, robotics, and gaming. With large-scale image datasets available for human fixation, it is now possible to train deep neural networks for generating a fixation map. Human fixations are a function of both local visual features and global context. We incorporate this in a deep neural network by using global and local features of an image to predict human fixations. We sample multi-scale features of the deep residual network and introduce a new method for incorporating these multi-scale features for the end-to-end training of our network. Our model DeepAttent obtains competitive results on SALICON and iSUN datasets and outperforms state-of-the-art methods on various metrics.
Analysis of different background subtraction methods applied on drone imagery under various weather conditions in the UAE region

The following paper presents a benchmarking study of the performance of thirty state of the art background subtraction algorithms. In this work, we test the performance of multiple background subtraction methods using drone imageries taken under various weather conditions in the UAE region. This is done by comparing the quality of the foreground mask that is extracted when using these algorithms. Visual Studio and MATLAB has been used to perform the comparison simulations, which would give us a comprehensive background subtraction study to indicate the advantages and disadvantages of each of the algorithms. The algorithms must be robust to stabilization errors, able to cope with insufficient information as a result from various weather conditions such as wind, haze and heat, and able to cope with dynamic backgrounds.
Research on Cloud Platform for Safety Supervision and Early Warning of Small Reservoirs

In order to meet the urgent needs of safety supervision and early warning of small reservoirs in China, and take the advantages of modern information technology in automation and intelligence, the overall design of cloud platform for safety supervision and early warning of small reservoirs is carried out from two aspects, one is based on cloud technology platform architecture, the other is the discussion of trusted operation mode. Small reservoirs have some daily management problems, such as limited funds, remote and complex environment, and weak technical force. This paper analyses and implements the core content and key technologies of platform construction, such as integrated intelligent monitoring system, platform business application system, and professional service. The informationization means and preliminary scheme for safety supervision and early warning of small reservoirs with high stability, high cost performance and scientific advancement are put forward.
Agile Solution of Color Image Encryption Using Random Permutation Algorithm

The requirements of Internet security are likely to be increased while technology is developing hour by hour. It's the reason why nowadays scientists are trying to study, invent solutions to prevent hacking, personal data leakage and make an effort to solve many other troubles. Among them, image encryption is also a very crucial challenge in 4.0th Industry Revolution period. In this paper, a simple but effective solution is proposed. The spirit of this one is to contribute a solution that the parameter's locations of an image will be changed continuously at every turn of transmission activities. Pixels parameters locations will be mixed-up together irregularly. Besides, the key number values that using for decryption process are changed and hid right into the image matrix. The experiment result shows that there are might be a lot of development base on this idea that can be extended to encrypt an image instead of using one formula to change entire of the picture at one time. In fact, the content is not focused on the complication or breakthrough of the method. This paper focuses on the coordination, development and expansion capabilities. That's Agility.
Tracking of Objects on Images: a General Approach

The article describes the approach to the construction of algorithms for tracking objects on the video image. Details are commented on the implementation of the procedure for associating traces of marks on adjacent frames using metrics. The results of modeling the association algorithms are given.
Constructing a One-time and a Few-time Digital Signature Schemes from the Hardness of Megrelishvili Vector-Matrix Problem

In this paper we develop a one-time and a few-time digital signature schemes by modifying the process of the authenticated Megrelishvili key exchange. The security of our schemes is based on the hardness of the Megrelishvili Vector-Matrix Problem (MVMP). We prove the correctness of our schemes mathematically and argue that they are at least as secure as the authenticated Megrelishvili protocol. Moreover, we demonstrate that both signature creation and verification take polynomial amount of finite field operations. Our few-time signature scheme allows a restriction to the message owner so that he can only certify at most n messages whenever the vector space F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> is used. We consider this feature valuable when we want to limit the number of messages that can be issued by the message owner.
Real Time 2D Shape and Object Recognition: A Child Learning Approach

It is very essential to identify shapes of different objects surrounding us. Specially the early learner children should be able to recognize the geometric shape of any object at a first glance. In this system, we are proposing a method to identify both the geometric shape and object recognition when the child will bring the item nearer to the web camera. Generally it is seen that most of the time shape and object recognition are done in scattered and lengthy way. But in this proposed work we have used two modules for identification and recognition inside one methodo. Canny edge detection algorithm is used to distinguish and check the geometric shape of an object in real time. We have also used SSD and mobilenet to classify some of the common objects.
Identifying Lexical Bundles for an Academic Writing Assistant in Spanish

This paper presents the process of identifying recurrent multi-word expressions (i.e. lexical bundles) relevant for a writing-aid of academic texts in Spanish. It also proposes a repertory of discourse functions that enables the classification of the candidate word-strings as well as their onomasiological retrieval. This classification into discourse functions is also key in the process of selecting candidates, as those that do not fit in any function will predictably be of little interest in the context of the mentioned writing aid. Through the examination of the resulting data, the study explores the correlation between candidate selection by lexicographers and several association measures proposed in the literature to obtain high-quality lexical bundles, with a view to assess the feasibility of automating this process.
Object Labeling in 3D from Multi-view Scenes Using Gaussian–Hermite Moment-Based Depth Map

Depth as well as intensityEnan, Sadman Sakib of a pixel plays a significantMahbubur Rahman, S. M. role in labeling objectsHatzinakos, Dimitrios in 3D environmentsHaque, Samiul. This paper presentsHowlader, Tamanna a novel approach of labeling objects from multi-view video sequences by incorporating rich depth information. The depth map of a scene is estimated from focus-cues using the Gaussian–Hermite moments (GHMs) of local neighboring pixels. It is expected that the depth map obtained from GHMs provides robust features as compared to that provided by other popular depth maps such as those obtained from Kinect and defocus cue. We use the rich depth and intensity values of a pixel to score every point of a video frame for generating labeled probability maps in a 3D environment. These maps are then used to create a 3D scene wherein available objects are labeled distinctively. Experimental results reveal that our proposed approach yields excellent performance of object labeling for different multi-view scenes taken from RGB-D object dataset, in particular showing significant improvements in precision–recall characteristics and F1-score.
Study on division of groundwater function zones in Pingtan Island

Abstract Based on the topography, geomorphology, groundwater exploitation and other hydrogeological data of Pintan Island, the study area was divided into three levels and basic evaluation units were divided. According to the zoning principle of Technical Outline of Groundwater Function Zoning in China, and through the spatial analysis function of ArcGIS, the zoning index data was spatialized and the overlay analysis was carried out. The study area was divided into three types of first-level function zones and eight types of second-level function zones. Moreover, the groundwater function zones map of the study area was drawn. Relevant results can provide reference and basis for relevant departments to develop, utilize and protect groundwater.
Asymptotic Load Balancing Algorithm for Many Task Scheduling

Cloud computing can enable the unraveling of new scientific breakthroughs. We will eventually arrive to compute overwhelmingly large sizes of information, larger than we ever thought about it. Better scheduling algorithms are the key to process Big Data. This paper presents a load balancing scheduling algorithm for Many Task Computing using the computational resources from Cloud, in order to process a huge number of tasks with a finite number of resources. As such, the algorithm can be also used for Big Data, because it scales easily for big applications if we put a load balancing algorithm on top of virtual machines. We impose an upper bound of one for the maximum nodes that can carry an arbitrary job without executing it and we show that this statement holds by simulating the algorithm in MTS2 (Many Task Scheduling Simulator). We also show that the algorithm's overlay performs even better when there are multiple nodes and we discuss about choosing the best local scheduling policy for the working nodes.
The Time-Course of Phoneme Category Adaptation in Deep Neural Networks

Both human listeners and machines need to adapt their sound categories whenever a new speaker is encountered. This perceptual learning is driven by lexical information. In previous work, we have shown that deep neural network-based (DNN) ASR systems can learn to adapt their phoneme category boundaries from a few labeled examples after exposure (i.e., training) to ambiguous sounds, as humans have been found to do. Here, we investigate the time-course of phoneme category adaptation in a DNN in more detail, with the ultimate aim to investigate the DNN’s ability to serve as a model of human perceptual learning. We do so by providing the DNN with an increasing number of ambiguous retraining tokens (in 10 bins of 4 ambiguous items), and comparing classification accuracy on the ambiguous items in a held-out test set for the different bins. Results showed that DNNs, similar to human listeners, show a step-like function: The DNNs show perceptual learning already after the first bin (only 4 tokens of the ambiguous phone), with little further adaptation for subsequent bins. In follow-up research, we plan to test specific predictions made by the DNN about human speech processing.
Key Life Cycle and Estimation of Time for Revocation of Keys in Cryptographic Systems

Key management model is a framework which provides the guidelines for generation, activation, distribution, use, deactivation, revocation, suspension, update, storage, destruction etc. for cryptographic keys. Key revocation is one of the essential requirement to revoke the key before scheduled expiration or existing key is no more trusted. Revocation of key is needed to limit the amount of information to be encrypted by any key, protects from various attacks, limits the system exposure, avoids certain cryptographic catastrophic failures (e.g. AES GCM mode loses protection if more than 64 GB is encrypted on the same key), protects against current or future algorithmic weakness that reduce key lifespan etc. To manage the keys efficiently, key management framework plays an important role. Framework defines the life of the key and also provides the guidelines how to manage the key during the various stages. In this paper, key management life cycles (Symmetric and Asymmetric) and Key revocation model to revoke the keys are proposed.
NEW APPROACHES TO TEACHING FOREIGN INFORMATION RESOURCES AT THE UNIVERSITY

Purpose. The present work investigates the impact of the pedagogy approaches in the teaching e-resource of low-motivated students. Materials and Methods . The pilot study involves about 1000 first-year undergraduates of the North-West Institute of Management of the Russian Presidential Academy of National Economy and Public Administration (NWIM RANEPA). Introductory part of the lecture is traditional. “Motivational” part varies depending on e-resource and includes a demonstration of all possible advantages for target audience — uniqueness, quality, advantages over open resources. In addition, the material is organized so as to provide multiple entries into the interface. Results . We studied statistics of visits to e-resources for 2 calendar years (before the introduction of new methods in 2016 and after — in 2017), and conducted surveys of students at the end of training. We got the apparent increase of visits when the pedagogical approach and the methods of training are changed. Discussion and conclusions . Research findings have illustrated the importance of support to the low-motivated students in their educational process by new approaches to the training. The librarian-teacher in this role is the link between the e-resource and the consumer, and their role is important for achieving the best results for both the organization and students. The authors are conscious that the interest in subscription resources would probably decline in the future. The keeping this interest is beyond the scope of this study. The data obtained and the conclusions made can be useful at all stages of the students’ training at any year of study.
Modern Mobile Emergency Applications: Fact or Fiction?

In this paper we explore how mobile emergency applications compare to existing means of reaching emergency services, and outline some important problems blocking their more widespread adoption. Many problems, such as roaming and standardized data exchange between the citizen and the public safety answering point (PSAP) were recently addressed in the Pan-European Mobile Emergency Application (PEMEA) standard; some remain strictly in the domain of the vendors, for example, deep integration with the mobile operating systems. However, a broader set of problems relates to communication and mobile networks: the way such calls are charged, prioritized, and integrated with the back-office systems of the telecom providers. We explore how some of these challenges could be addressed in 5G networks with their strong focus on softwarization, virtualization, network slicing and mobile edge computing. Finally, we also address several business-related challenges, such as user awareness, application discoverability and usability, and present our attempt at addressing low user awareness in critical situations.
A Degree-Based Analysis of ‘V+A+le2’ Construction

This paper examines both the result-realization and the result-deviation interpretations of ‘V+A+le2’ construction under the framework of degree semantics. It is argued that the ‘V+A+le2’ construction is a comparative construction and that different interpretations arise because different standards of comparation are adopted. Result-realization interpretations are seen in all instances of the construction while result-deviation interpretations can be found only in instances with open-scale adjectives. The syntactic structure determines whether the ‘A’ encodes a measure function or the ‘V+A’ encodes a measure of change function in the ‘V+A+le2’ comparative construction.
On Including the Institutional Review Board Process in Undergraduate Information Technology Curricula

This lightning talk discusses the institutional review board (IRB) and the Information Technology (IT) education community aiming to a) illuminate the benefits that our IT students and community can gain from the IRB process and b) have a general discussion of the role the IRB process should have in IT curricula and specific IT courses.
Different Goal-driven CNNs Affect Performance of Visual Encoding Models based on Deep Learning

A convolutional neural network with outstanding performance in computer vision can be used to construct an encoding model that simulates the process of human visual information processing. However, training goal of the network may have impacted the performance of encoding model. Most neural networks used to establish encoding models in the past were performed image classification task, the task of which is single. While in the process of human's visual perception, multiple tasks are performed simultaneously. Thus, the existing encoding model does not well satisfy the diversity and complexity of the human visual mechanism. In this paper, we first established a feature extraction model based on Fully Convolutional Network (FCN) and Visual Geometry Group (VGG) with similar network structure but different training goal, and employed Regularize Orthogonal Matching Pursuit (ROMP) to establish the response model, which can predict the stimuli-evoked responses measured by functional magnetic resonance imaging (fMRI). The results revealed that the convolutional neural networks trained by different visual tasks had significant difference in the performance of visual encoding with almost the same network structure. The VGG-based encoding model can achieve a higher performance in most voxels of ROIs. We concluded that classification task in computer vision can better fit the visual mechanism of human compared to visual segmentation task.
A Heuristic Hybrid Recommended Order Modle

As the informationized trend is developing toward intelligentized one in catering industry, the targeted ordering recommendation provide for customer based on the technique of intelligent recommendation has turned into reality. Applying in the scenario of traditional Chinese food service, a heuristic and hybrid model in ordering recommendation is proposed. First, the association rules algorithm was adopted for obtaining the association rules of historically associated dishes combination and calculating their correlation degree. Second, applying the recommended algorithm based on dishes attributes to calculate the similarities of dishes in the database. Third, to calculate comprehensive scores of dishes and create the recommendation rules in accordance with their correlation degree and similarities. Finally, a recommended order list is shaped from both the dishes ordered and the recommendation rules concluded in the former step. The effectiveness and validation of the model and algorithm are being proved by real order data in Chinese restaurants. The data shows the model is better than traditional association rules in the aspects of recommendation precision and coverage when the dishes ordered reaching to certain amount.
A Context-Free Spelling Correction Method for Classical Mongolian

Spelling errors in the classical Mongolian text are mainly caused by misuse of polyphonic letters which present the same shape in the certain position of the word. About half to three-quarters of the classical Mongolian words are misspellings which have the correct appearances but wrong codes. In this paper, we code the Mongolian words by glyph codes to map the words to their shapes one-to-one. In addition, we also proposed the correction of out-of-vocabulary words (OOV) based on the Evolved Transformer by formalizing the correction task as a translation from misspellings to target spellings. The experimental results show that this approach achieves the new state-of-the-art performance.
Balancing Theory and Practice in IT Curriculum

Undergraduate and graduate students in information technology need a balance between theory and practice to be prepared and ready for the workforce and long-life self-learning. What are the core IT principles students from developing countries should learn? How are the undergraduate and graduate information technology curricular and training requirements different between US and other developing countries? This lightning talk discusses the differences in the graduate and undergraduate IT training requirements between US and developing countries.
Context Block Estimation for Random Fields

For random fields on the d-dimensional integer lattice with finite state space, the basic neighborhood is the smallest region around a site that determines the conditional distribution at the site given the values at all other sites. For variable basic neighborhood random fields the basic neighborhood may vary with the values at the surrounding sites, and the sets of the values in these basic neighborhoods are called context blocks. The context blocks may be infinite. Statistical estimation of the context block system of a random field from a sample, a single realization of the random field observed in a finite region, is addressed. The Optimal Likelihood Ratio (OLR) estimator is introduced. Its nearly linear computation complexity is shown and a bound on the probability of the estimation error is proved that implies strong consistency of the estimator.
Video Segmentation Based on SURF Flow Field

In recent days, the automatic extraction of salient object regions from video data is the most significant for visual analytical solutions. It also brings up more challenges, including pose differences amongst background and foreground objects, motion patterns etc. In this project, a framework of co-segmentation is presented in order to discover or segment the objects in a joint fashion from multiple videos and frames. Feature extraction is one of the main steps in object detection. For this feature extraction, Here introduce a speed up Robust features (SURF) flow descriptor to integrate the object features from the video frames in an optical manner. This novel SURF flow can extract foreground object over the complete video dataset.
Using Simple Games to Evaluate Self-Organization Concepts: a Whack-a-mole Case Study

We present the idea of using variants of simple games as an easy additional application area to establish generality of AI concepts. We substantiate this idea by using multi-hammer Whack-a-mole as application area for the efficiency improvement advisor and extended efficiency improvement advisor concepts for self-organizing multi-agent systems. Both concepts have previously been applied to pickup-and-delivery problems and were claimed to be general concepts for improving solving dynamic task fulfillment problems. Our experiments with multi-hammer Whack-a-mole show similar improvements to the other area for both concepts, giving credit to the generality claim.
Building of Information Activity Management System of Higher Educational Establishment in the Social Environments of the Internet

The paper presents the architecture of the software-algorithmic complex for management activities providing of administrative and information subdivisions of the HEE and information activities of the basic subdivisions, the main components of the information system, as well as functions and technical aspects of the implementation individual components are described.