Reviewerly/exHarmony huggingface dataset:
Big Data Management Using Hadoop

Abstract Today, one of the key issues is the design of systems and software to deal with the storage, management and processing of large amounts of data as a result of the exponential rise in data. In unstructured forms, these data are found. Due to the large and complex data sizes, data management with traditional approaches is unacceptable. Hadoop is an appropriate solution for the continuous growth of data sizes. We have suggested in this paper techniques and algorithms dealing with big data including data collection, preprocessing of data. The Fragmentation algorithm will take the function of a distributed implementation of the traditional file system time-sharing model, where various users share files and storage resources. Also, in this research we used a framework to improve the performance of a query and reduce the response time called the HADOOP. The Apache Hadoop project for safe, scalable and distributed computing. The results showed that Hadoop is the best way to deal with big data during calculating the rate of response time of a complex query for example at (00:00:01) per second and comparing it with the response time of the same queries on the fragmentation algorithm at (00: 01:11) per second and the standard database at (00:05:13) per second. We concluded that Total time Access for complex queries in distributed processing is faster than in non-distributed processing.
Smart System with Artificial Intelligence for Sensory Gloves

This paper presents a new sensory system based on advanced algorithms and machine learning techniques that provides sensory gloves with the ability to ensure real-time connection of all connectors in the cabling of a cockpit module. Besides a microphone, the sensory glove also includes a gyroscope and three accelerometers that provide valuable information to allow the selection of the appropriate signal time windows recorded by the microphone of the glove. These signal time windows are subsequently analyzed by a convolutional neural network, which indicates whether the connection of the components has been made correctly or not. The development of the system, its implementation in a production industry environment and the results obtained are analyzed.
Soft Skills Development in Computer Science Students via Multinational and Multidisciplinary GameDev Project

The development of communication and other soft-skills among computer science students is not an easy task. Most curricula focus on technical skills. Team project methodologies are often used as a way to improve students? communication skills. However, the student teams are usually only comprised of computer science students. This paper is an experience report of the didactical methodology used in a project called [email protected], which has run annually since 2014. The project is short, intensive and team-based, with an important requirement that each student team is multinational and multidisciplinary. In the project, university lecturers act as mentors who help the student teams organize their work and tracks if a team?s planned didactical results are being achieved. The [email protected] project stimulates an increase of soft-skills among IT and other students who participate in the project. The detailed description of our process will allow others to recreate the project or build similar events in colleges or even company environments.
GreenCloudNet++: Simulation framework for energy efficient and secure, green job scheduling in geographically distributed data centers

Abstract Geographically distributed data centers are used as backbone infrastructure to meet rapidly increasing service demands of computations and data storage in cloud computing. This increase results in high energy consumption, increased operational expenditures, and high carbon footprint which are becoming points of great concern for service providers. In this research work, we present a simulation framework named GreenCloudNet++ for simulation and evaluation of energy‐efficient, green‐aware, and secure job scheduling mechanisms for geographically distributed data centers. GreenCloudNet++ has been developed as an extension to CloudNetSim++, which is designed to simulate distributed data center architectures connected with high speed networks. The functionality of CloudNetSim++ is extended by adding hierarchical job scheduling mechanism, hierarchical statistics collection mechanism and integration mechanism for green energy. Proposed model considers availability of green energy at each data center and maximizes its utilization. It considers the amount of underutilized computational resources at individual data centers while assigning jobs to the data centers, which helps to achieve better server consolidation resulting in better energy efficiency. Proposed model also relies on network load inside each data center which helps avoiding hotspots.
DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences

This paper presents a novel unsupervised probabilistic model estimation of visual background in video sequences using a variational autoencoder framework. Due to the redundant nature of the backgrounds in surveillance videos, visual information of the background can be compressed into a low-dimensional subspace in the encoder part of the variational autoencoder, while the highly variant information of its moving foreground gets filtered throughout its encoding-decoding process. Our deep probabilistic background model (DeepPBM) estimation approach is enabled by the power of deep neural networks in learning compressed representations of video frames and reconstructing them back to the original domain. We evaluated the performance of our DeepPBM in background subtraction on 9 surveillance videos from the background model challenge (BMC2012) dataset, and compared that with a standard subspace learning technique, robust principle component analysis (RPCA), which similarly estimates a deterministic low dimensional representation of the background in videos and is widely used for this application. Our method outperforms RPCA on BMC 2012 dataset with 23% in average in F-measure score, emphasizing that background subtraction using the trained model can be done in more than 10 times faster (The source code is available at: https://github.com/ostadabbas/DeepPBM).
Proof-of-Concept (POC) of Restaurant’s food requests in the Lisk Blockchain/sidechain

Abstract Request food online is daily present in the life of people, however, it is necessary to send delivery information to different restaurant systems and sometimes these restaurants share customer sensitive information with their partners without the authorization of the customer creating a security risk with customers data. Also, in most cases when a person travels to a different country and wants to request food online, it is necessary to use the local currency, requiring a currency exchange operation. This paper evaluates a proof-of-concept (POC) of the integration of blockchain in the food industry, allowing customers to order food online through a sidechain, an exclusive blockchain with specific custom transaction types for restaurants. The FoodTransaction type is responsible to order food and store cryptographically customer sensitive information that only the customer itself and the requested restaurant can read, hence it respects customer privacy. Furthermore, the blockchain cryptocurrency can be used anywhere in the world. The performance evaluation shows a great advance in throughput when compared to the use of the payment solution on Bitcoin applications. Finally, the results show the possibility to extend the restaurant sidechain business to several restaurants in the world and in different time zones.
"What's this?" Comparing Active learning Strategies for Concept Acquisition in HRI

Social robotics aim to equip robots with the ability to exhibit socially intelligent behaviour while interacting in a face-to-face context with human partners. An important aspect of face-to-face social interaction includes the efficient recognition of their surroundings, the environment and the objects within it, so as to be able to discuss, describe and provide instructions to assist continuous collaboration between the speaker and the listener. Although humans can efficiently learn from their interlocutors to perceptually ground word meanings of visual objects from just a single example, teaching robots to ground word meanings remains a very challenging, expensive and resource-intensive task. In this paper, we present a novel framework for robot concept acquisition on the fly, by combining few-shot learning with active learning. In this framework, a robot learns new concepts through collaboratively performing tasks with humans. We compared different learning strategies in a task-based evaluation with human participants, and we found that active learning significantly outperforms a non-active learning alternative, and is more preferable by the participants while increasing their trust in the social robot's capabilities.
Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization

Stochastic convex optimization, by which the objective is the expectation of a random convex function, is an important and widely used method with numerous applications in machine learning, statistics, operations research, and other areas. We study the complexity of stochastic convex optimization given only statistical query (SQ) access to the objective function. We show that well-known and popular first-order iterative methods can be implemented using only statistical queries. For many cases of interest, we derive nearly matching upper and lower bounds on the estimation (sample) complexity, including linear optimization in the most general setting. We then present several consequences for machine learning, differential privacy, and proving concrete lower bounds on the power of convex optimization–based methods. The key ingredient of our work is SQ algorithms and lower bounds for estimating the mean vector of a distribution over vectors supported on a convex body in R d . This natural problem has not been previously studied, and we show that our solutions can be used to get substantially improved SQ versions of Perceptron and other online algorithms for learning halfspaces.
Dual-pathway Attention based Supervised Adversarial Hashing for Cross-modal Retrieval

Due to the success of deep learning in recent years, cross-modal retrieval has made significant progress. However, there is still a key challenge: how to learn the correlation between different modality data more effectively to improve the retrieval accuracy. Therefore, in this paper we proposed a Dual-pathway Attention based Supervise Adversarial Hashing (DASAH) to obtain a unified cross-modal semantic representation. Based on Dual-pathway attention, that is, learning the attention of image regions (text sequences) to text sequences (image regions), the fine-grained semantic correlation between different modality data is deeply mined, and the adversarial learning is integrated to further improve the learning ability of cross-modal semantic correlation. The model makes full use of dual-pathway attention to guide fine-grained cross modal feature learning, and integrates fine-grained cross modal feature learning and adversarial hashing learning in a unified framework for joint learning and optimization. Extensive empirical studies show that the proposed method outperforms several state-of-the-art methods for cross-modal retrieval.
TigeCMN: On exploration of temporal interaction graph embedding via Coupled Memory Neural Networks

With the increasing demand of mining rich knowledge in graph structured data, graph embedding has become one of the most popular research topics in both academic and industrial communities due to its powerful capability in learning effective representations. The majority of existing work overwhelmingly learn node embeddings in the context of static, plain or attributed, homogeneous graphs. However, many real-world applications frequently involve bipartite graphs with temporal and attributed interaction edges, named temporal interaction graphs. The temporal interactions usually imply different facets of interest and might even evolve over the time, thus putting forward huge challenges in learning effective node representations. Furthermore, most existing graph embedding models try to embed all the information of each node into a single vector representation, which is insufficient to characterize the node's multifaceted properties. In this paper, we propose a novel framework named TigeCMN to learn node representations from a sequence of temporal interactions. Specifically, we devise two coupled memory networks to store and update node embeddings in the external matrices explicitly and dynamically, which forms deep matrix representations and thus could enhance the expressiveness of the node embeddings. Then, we generate node embedding from two parts: a static embedding that encodes its stationary properties and a dynamic embedding induced from memory matrix that models its temporal interaction patterns. We conduct extensive experiments on various real-world datasets covering the tasks of node classification, recommendation and visualization. The experimental results empirically demonstrate that TigeCMN can achieve significant gains compared with recent state-of-the-art baselines.
An Enhanced Method for Information Hiding Using LSB Steganography

Abstract This work involves a method to communicate one to one by hiding the information among all the people. Digital photographs are the most suitable components compared to other things available on the internet as transmitter based on their availability. This work basically ciphered message within a photographs. For hiding secret information in images, there exist an outsized sort of steganographic techniques some are more complex than others and every one of them have respective strong and weak points. Different applications may have separate requirements of the cipher technique used. for instance, some applications may require absolute invisibility of the key information, while others require a bigger secret message to be hidden. This work intends to offer an summary of image steganography, its uses and techniques. LSB (Least Significant Bit) technique is used for this work. Here bits of the text are replaced by the LSBs of the pixel values of the image. because the LSBs are alone changed, normal human eyes cannot predict the difference between the first image and therefore the resulting image. In this work, the pixel indicator technique has been used to hide information in RGB Images. Least two significant bits are used for this proposed methodology as of one for the channels of Red, Green or Blue as an indicator of data existence in the other two channels. Based on the image nature indicator bits will be generated randomly in the channel.
An Improved Density Based Support Vector Machine (DBSVM)

Support Vector Machines (SVM) is a classification model based on the duality optimization approach. Non-zero Lagrange multipliers correspond to the data selected to be support vectors used to build the margin decision. Unfortunately, SVM has two major drawbacks: the noisy and redundant data cause an overfitting; moreover, the number of local minima increases with the size of data, even worse when it comes to Big Data. To overcome these shortcoming, we propose a new version of SVM, called Density Based Support Vector Machine (DBVSM), which performs on three steps: first, we set two parameters, the radius of the neighborhood and the size of this latter. Second, we determine three types of points: noisy, cord and interior. Third, we solve the dual problem based on the cord data only. To justify this choice, we demonstrate that the cord points cannot be support vectors. Moreover, we show that the kernel functions don't change the cord point nature even. The DBSVM is benchmarked on several datasets and is compared with a variety of methods in the literature. The results of the tests prove that the proposed algorithm is able to provide very competitive results in terms of time, classification performance, and capacity to tackle datasets of very large size. Finally, to point out the consistency of the DBSVM, several tests were performed for different values of the ratio and the neighborhood size.
Multiscale Unfolding: Illustratively Visualizing the Whole Genome at a Glance

We present Multiscale Unfolding, an interactive technique for illustratively visualizing multiple hierarchical scales of DNA in a single view, showing the genome at different scales and demonstrating how one scale spatially folds into the next. The DNA's extremely long sequential structure-arranged differently on several distinct scale levels-is often lost in traditional 3D depictions, mainly due to its multiple levels of dense spatial packing and the resulting occlusion. Furthermore, interactive exploration of this complex structure is cumbersome, requiring visibility management like cut-aways. In contrast to existing temporally controlled multiscale data exploration, we allow viewers to always see and interact with any of the involved scales. For this purpose we separate the depiction into constant-scale and scale transition zones. Constant-scale zones maintain a single-scale representation, while still linearly unfolding the DNA. Inspired by illustration, scale transition zones connect adjacent constant-scale zones via level unfolding, scaling, and transparency. We thus represent the spatial structure of the whole DNA macro-molecule, maintain its local organizational characteristics, linearize its higher-level organization, and use spatially controlled, understandable interpolation between neighboring scales. We also contribute interaction techniques that provide viewers with a coarse-to-fine control for navigating within our all-scales-in-one-view representations and visual aids to illustrate the size differences. Overall, Multiscale Unfolding allows viewers to grasp the DNA's structural composition from chromosomes to the atoms, with increasing levels of "unfoldedness," and can be applied in data-driven illustration and communication.
A Grammatical Evolution Approach for Estimating Blood Glucose Levels

The management of diabetes is a very complex task, hence devising automatic procedures able to predict the glycemic level can represent a significant step towards the building of an artificial pancreas capable of providing the needed amounts of insulin boluses.This paper presents a Grammatical Evolution-based algorithm aiming at extrapolating a regression model able to estimate the blood glucose level in future instants of time through interstitial glucose measurements. The hypothesis is that the amounts of carbohydrates assumed, of basal insulin levels and of those administered with boluses are known. Experiments, performed on a real-world database made up of five patients suffering from Type 1 diabetes, are shown in terms of Clark Error Grid analysis. To evaluate the effectiveness of the predictions derived from the proposed approach, the results obtained are compared against those obtained by other state-of-the-art evolutionary-based methods very recently proposed.
Deep Reinforcement Learning of Cooperative Control with Four Robotic Agents by MADDPG

Due to the nature of complexity, inflexibility and non-robustness of classical cooperative control algorithms, the deep reinforcement learning has been widely researched and applied in collective and continuous behaviour control. Especially for multi-agents in real world, acquiring a full view world with a quick learning is still a great challenge. Inspired by Policy Gradient (PG) and its successors, a toy model with multi-agents by four two-dimensional manipulators environment is built based on physics engine-based MuJoCo. With a modified deep deterministic policy gradient algorithm and different credit strategies for individual agent, the cooperation and competition behaviour to target location between agents are studied. The experimental results show that each robot can complete the task with a negligible convergence effect, indicating that the MADDPG algorithm has a good performance in a complex environment, and successfully learn the strategy of multi-agent collaboration. However, with the instability of the environment caused by the increase in the number of agents, deep reinforcement learning has certain difficulties in the joint action space.
WITHDRAWN: Lesion detection in brain MRI using PSO based segmentation

In modern clinical diagnosis image processing plays a significant role. Digital imaging of vital organs, their processing and analysis is one of the most important areas of research and development to get maximum accuracy in diagnosis. Especially now a days it is an integral part of medical diagnosis. The different techniques applied is begins with radiology to ultra-sonograph, Magnetic Resonance Imaging (MRI) etc. in many preliminary clinical diagnosis images of the effected organ is the one of effective way of diagnosis-process. The different state of the art digital imaging equipment is introduced to capture and render the images of the internal body organs of the patient to carry the diagnostic process more accurate. To gather more relevant diagnostic data, the electronically captured digitized images need to be fine-tuned digitally. The various methods and techniques involved in fine tuning the images are – cleaning the image by removing the noise, separating different shades by segmenting the image and getting the threshold-point etc. Segmenting an image is a method to separate the marked area of an image from the rest of image to single out the marked-area more conspicuously. There are many unique techniques evolved to execute the segmentation operation, one of them is - Image Thresholding. There are many innovative implements are also introducing to execute the steps of image thresholding. Here we use a recent introduced Particle Swarm Optimization (PSO) tool to segment the brain MRI to detect the brain-lesions using image thresholding technique.
Blockchain Technology and Higher Education: Characteristics, Dilemma and Development Path

Blockchain technology have a significant impact on higher education informatization inevitably in the future. This paper analyzes influence of blockchain technology on higher education which contains 4 aspects on the basis of connotation and characteristics of blockchain technology, that is data, trade, application and system, combines with present development dilemma which is credit management tool missing, mass data storage bottleneck, unstable system performance and low efficiency management system, puts forward development path accordingly which is reconstruction education trust mechanism, creation efficient transaction path, optimization construction of education system and reforming existing management system, which hopes to provide reference for promotion integration of blockchain technology and higher education.