Reviewerly/exHarmony huggingface dataset:
Image Steganography Using Discrete Cosine Transform Algorithm for Medical Images

The number of medical images is increasing exponentially day-by-day. This comes with increase in the security measures to be taken. All these medical images need to be shared among the doctors and the hospitals, so we have to take special care about the patient’s privacy not being exploited. Thus, digital steganography is a good way to protect the patient’s privacy. The quality of the image can’t be compromised. The information of the patient embedded in the image should be easily retrievable, and the process should not decompose the image in any manner. The steganography must be robust. So, only the authorized person can know about the existence of some hidden data. This paper aims at steganography discrete cosine transform (DCT), and the stimulated outputs we got.
Support Outsourcing Unsigncryption and Member Revocation Identity-based Proxy Signcryption Scheme with Drone Environment

Abstract The identity-based signcryption scheme combines the advantages of the signcryption scheme and the identity-based cryption scheme, which has high calculation and communication efficiency and easy key management. In order to effectively solve the problems of high latency and data information privacy during the secure transmission of drone data to cloud servers, we propose an identity-based proxy signcryption scheme that supports outsourcing decryption and member revocation. The complex bilinear pairing calculation is outsourced to the cloud server to reduce the user’s computing overhead. When an illegal user wants to access the data, the illegal user’s identity value ID will be put into the revocation list and the update private key algorithm is executed to generate an update password for the unrevoked user Key, revoked users lose the ability to decrypt because they cannot obtain the updated key that matches them. The identity-based signcryption scheme protected by the proxy can satisfy unforgeability and forward security. Finally, the comparison results show that this solution is superior to previous work in terms of function and calculation time.
A Distributed Multithreaded Evolutionary Computing Frame Work using Differential Evolution Algorithm

Differential Evolution (DE), the evolution algorithm (EA) in the repository of evolutionary computing (EC), is known for its simplicity and adaptability to high performance computing paradigms. The distributed algorithmic frameworks of DE are the natural extension of DE, as DE is highly suitable for parallel and distributed computing. This paper proposes a study on implementing an enhanced distributed differential algorithm framework (named as mtdDE - multithreaded distributed DE) with multi-threaded islands in a distributed framework, to attain maximum level of data parallelism. The main objective of mtdDE is to reduce the computation time and to improve the solution quality for the targeted optimization problems. This paper presents this study in two phases. The Phase I implements the traditional distributed DE (dDE) framework and compares its performance with a state-of-the-art distributed DE, on a set of 8 benchmarking functions. In Phase II, the proposed mtdDE was implemented and its performance was compared with the dDE on a set of 4 benchmarking problems. The performance metrics considered were - the objective function values (ofv), the mean objective function values (mov), the execution time (et) and the average execution time (aet). The results acquired for the performance metrics of the dDE and mtdDE were studied, and the study revealed that the mtdDE outperforms the dDE in terms of both the solution accuracy and the computation time. The detailed discussion about the algorithmic structure, the experimental setup, the results obtained and the comparative study were presented in this paper.
Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality

Artificial neural networks (ANNs) have become a very powerful tool in the approximation of high-dimensional functions. Especially, deep ANNs, consisting of a large number of hidden layers, have been very successfully used in a series of practical relevant computational problems involving high-dimensional input data ranging from classification tasks in supervised learning to optimal decision problems in reinforcement learning. There are also a number of mathematical results in the scientific literature which study the approximation capacities of ANNs in the context of high-dimensional target functions. In particular, there are a series of mathematical results in the scientific literature which show that sufficiently deep ANNs have the capacity to overcome the curse of dimensionality in the approximation of certain target function classes in the sense that the number of parameters of the approximating ANNs grows at most polynomially in the dimension d∈N of the target functions under considerations. In the proofs of several of such high-dimensional approximation results it is crucial that the involved ANNs are sufficiently deep and consist a sufficiently large number of hidden layers which grows in the dimension of the considered target functions. It is the topic of this work to look a bit more detailed to the deepness of the involved ANNs in the approximation of high-dimensional target functions. In particular, the main result of this work proves that there exists a concretely specified sequence of functions which can be approximated without the curse of dimensionality by sufficiently deep ANNs but which cannot be approximated without the curse of dimensionality if the involved ANNs are shallow or not deep enough.
A New Blockchain-based Electronic Medical Record Transferring System with Data Privacy

From the current national-level medical diagnosis and treatment perspective, the paper-version prescription is still the only slip issued by the doctor to allow for the patient to take the prescribed medicine from the pharmacy store or clinic. However, the paper prescription is not easy to store and its confidentiality is low, if it is lost, it will cause unnecessary trouble to the patient. Therefore, the use of digital prescriptions has been considered for future electronic medical record reform. The challenges of digital prescriptions are also upfront, for instance, it contains a large amount of patient's medical record information and private information, how to effectively maintain the level of security while the premise of privacy has caused a series of hot issues, which become a major concerns of people who promote the use of digital prescriptions. In recent years, blockchain has been widely regarded as a promising online business data and transaction security technology. Due to its decentralization, security and credibility, collective maintenance, and non-tampering, it is widely used in data storage and sharing. This paper proposes a blockchain-based medical prescription system. At the same time, in order to effectively protect the privacy of patients, during this period we adapted the k-anonymity algorithm and incorporated a differential privacy protection mechanism to enhance its secured performance.
Similarity Score of Two Images using Different Measures

In the field of computer vision and image processing, image similarity has been a central concern for decades. If you compare two pictures, Image Similarity returns a value that tells you how physically they are close. A quantitative measure of the degree of correspondence between the images concerned is given by this test. The score of the similarity between images varies from 0 to 1. In this paper, ORB (Oriented Fast Rotated Brief) algorithm is used to measure the similarity and other types of similarity measures like Structural Similarity Index (SSIM), pixel similarity, Earth mover's Distance are used to obtain the score. When two images are compared, it shows how much identical (common) objects are there in the two images. So, the accuracy or similarity score is about 87 percent when the two images are compared.
FootApp: An AI-powered system for football match annotation

Abstract In the last years, scientific and industrial research has experienced a growing interest in acquiring large annotated data sets to train artificial intelligence algorithms for tackling problems in different domains. In this context, we have observed that even the market for football data has substantially grown. The analysis of football matches relies on the annotation of both individual players’ and team actions, as well as the athletic performance of players. Consequently, annotating football events at a fine-grained level is a very expensive and error-prone task. Most existing semi-automatic tools for football match annotation rely on cameras and computer vision. However, those tools fall short in capturing team dynamics and in extracting data of players who are not visible in the camera frame. To address these issues, in this manuscript we present FootApp, an AI-based system for football match annotation. First, our system relies on an advanced and mixed user interface that exploits both vocal and touch interaction. Second, the motor performance of players is captured and processed by applying machine learning algorithms to data collected from inertial sensors worn by players. Artificial intelligence techniques are then used to check the consistency of generated labels, including those regarding the physical activity of players, to automatically recognize annotation errors. Notably, we implemented a full prototype of the proposed system, performing experiments to show its effectiveness in a real-world adoption scenario.
Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning

Incremental learning is a methodology that continuously uses the sequential input data to extend the existing network’s knowledge. The layer sharing algorithm is one of the representative methods which leverages general knowledge by sharing some initial layers of the existing network. To determine the performance of the incremental network, it is critical to estimate how much the initial convolutional layers in the existing network can be shared as the fixed feature extractors. However, the existing algorithm selects the sharing configuration through improper optimization strategy but a brute force manner such as searching for all possible sharing layers case. This is a non-convex and non-differential problem. Accordingly, this can not be solved using powerful optimization techniques such as the gradient descent algorithm or other convex optimization problem, and it leads to high computational complexity. To solve this problem, we firstly define this as a discrete combinatorial optimization problem, and propose a novel efficient incremental learning algorithm-based Bayesian optimization, which guarantees the global convergence in a non-convex and non-differential optimization. Additionally, our proposed algorithm can adaptively find the optimal number of sharing layers via adjusting the threshold accuracy parameter in the proposed loss function. With the proposed method, the global optimal sharing layer can be found in only six or eight iterations without searching for all possible layer cases. Hence, the proposed method can find the global optimal sharing layers by utilizing Bayesian optimization, which achieves both high combined accuracy and low computational complexity.
Facial Expressions Recognition through Convolutional Neural Network and Extreme Learning Machine

This paper investigates the facial expressions recognition approach. The image classification process is based on deep learning techniques for detecting emotional expressions. This study involves two main parts. Firstly, it highlights the technique of extracting significant features resulting on the use of a pre-trained convolutional neural network (Alexnet model). Afterward, The extracted learned features are trained through a back propagation (BP) and an extreme learning machine (ELM) algorithms. All experiments are implemented on the two available JAFFE and KDEF datasets. Simulation results show that Alexnet is an effective model for features extraction and the ELM approach produces a better training and generalization performance with a faster learning speed to well detecting facial expressions.
Facial Recognition in Degraded Conditions Using Local Interest Points

In this paper we are interested in facial recognition, particularly in degraded conditions such as head pose variations, illumination, facial expressions and partial occlusions. In this context, several approaches have been used to overcome these problems and improve facial recognition. Our method consists in using a local approach based on interest points provided by the speeded-up-robust-feature descriptor for feature extraction and the k nearest neighbor combined with the k-dimensional tree for classification. The evaluation of the proposed approach on the Kinect Face DB and IST-EURECOM LFFD databases shows interesting results.
Performance Analysis and Comparison Among Different Task Scheduling Algorithms in Cloud Computing

In the field of cloud computing, efficient task scheduling plays a vital role. Cloud computing is an on-demand service where resources are shared virtually by the users to complete their tasks. The main concept of task scheduling is to execute the tasks in such an order that can enhance the performance with full resource utilization and minimum loss. Already a lot of scheduling algorithm exists targeting to various goals like reducing execution time, cost, makespan and increase resource utilization, load balancing, etc. In this work, we have mainly carried out the performance analysis and comparison among various well-known task scheduling algorithms. The algorithms are examined and implemented in a well-known cloud simulator (CloudSim) to observe their performances by varying different parameters. Finally, the performance metrics like makespan, waiting time, turnaround time, throughput, and load balancing are observed by plotting the results graphically. Along with the comparative analysis, this work also assists one to identify a better scheduling algorithm based on different scheduling constraints and criteria.
A new multi-decision based on Bayesian decision tree algorithm for ship recognition

Abstract Ship recognition is a key topic for intelligent shipping in canal. A new multi-decision algorithm based on Bayesian decision tree is proposed. The algorithm combine three Convolutional Neural Networks module - VGG, VGG19 and ResNet to get a better classify result. A quantization method is adopt to quantize the output of these network. A new method based on decision tree construct algorithm is proposed to improve the accurate of decision tree. In experiment, implement a various compared algorithms to prove the algorithm has better performance.
Evaluation of Graph-based Algorithms for Guessing User Recommendations of the Social Network Instagram

In this paper, we present an analysis of the user recommendation algorithm of the popular social network Instagram. First, we introduce a framework for the analysis and evaluation of user recommendations in any social network. Then, we build a neighborhood graph for selected active users on Instagram, containing more than 609,000 users and 747,000 relationships. For these users, we also create a dataset of not publicly available personalized recommendation lists. Finally, we apply several graph-based algorithms to this graph to identify Instagram's user recommendation system. We show that an algorithm based on Twitter's Who-to-Follow algorithm is most similar to Instagram's unknown algorithm when recommending users connected to the followees of the target users. Moreover, this algorithm is very cost-effective due to requiring only a relatively small, bipartite graph, compared to the other candidates. Furthermore, we give an analysis of the composition of the user suggestions based on the different reasons for the suggestions.
PowerNet: Learning-Based Real-Time Power-Budget Rendering

With the prevalence of embedded GPUs on mobile devices, power-efficient rendering has become a widespread concern for graphics applications. Reducing the power consumption of rendering applications is critical for extending battery life. In this paper, we present a new real-time power-budget rendering system to meet this need by selecting the optimal rendering settings that maximize visual quality for each frame under a given power budget. Our method utilizes two independent neural networks trained entirely by synthesized datasets to predict power consumption and image quality under various workloads. This approach spares time-consuming precomputation or runtime periodic refitting and additional error computation. We evaluate the performance of the proposed framework on different platforms, two desktop PCs and two smartphones. Results show that compared to the previous state of the art, our system has less overhead and better flexibility. Existing rendering engines can integrate our system with negligible costs.
CVRriculum Program Faculty Development Workshop: Outcomes and Suggestions for Improving the Way We Guide Instructors to Embed Virtual Reality Into Course Curriculum

Experiential education and student engagement are a main source of student attraction and retention in post secondary milieus.To remain innovative, it is imperative that universities look beyond the internet and traditional multimedia mediums and incorporate novel ways and cutting-edge technologies that can drastically change the way students and educators experience learning.The application of technology as an approach to experiential education is becoming more popular and has extensively impacted universities and other higher education organizations around the world.One approach to support this change in education delivery is to use immersive technologies such as virtual reality (VR).Our team has conducted a pilot study that focuses on embedding VR as a medium to teach empathy within higher education milieus.We began the study by conducting a pilot faculty development workshop to provide an understanding of VR and ways it can be embedded as a pedagogical approach to support curriculum design.Five faculty members from a local university were recruited to participate.Outcomes suggest that embedding VR into the curriculum is a feasible approach that provides an engaging learning environment that is effective for teaching an array of interpersonal skills.The workshop laid the foundation for future faculty training programs guiding the use of VR, prompting a dialog regarding plans for future workshops across a pan-university context.
Research on a cloud resource scheduling strategy based on asynchronous reinforcement learning

The effective management of cloud-based IT infrastructure resources plays an important role in the development of grid business and the reduction of operation and maintenance costs. For cloud resource scheduling, there are many factors that affect its performance, and it is difficult to use general methods to effectively solve the problem of cloud resource scheduling. In order to achieve efficient resource scheduling, this paper proposes a cloud resource scheduling strategy based on reinforcement learning. At the same time, in order to deal with the problem of slow convergence speed and low accuracy when the exploration and update of a single agent, By introducing a heterogeneous model to construct a cloud resource scheduling mechanism, which uses multithread to explore the environment at the same time to improve the convergence speed. Experiments show that the scheduling strategy of this method has better performance than the random scheduling strategy.
Software Engineering for Autonomous Robot: Challenges, Progresses and Opportunities

Software engineering for autonomous robot (SE4AR) is an emerging interdisciplinary field with the aims to support the development, running and evolution of software systems for autonomous robot - a safety and mission critical cyber-physical system. Such field has recently gained increasing attentions from both academia and industry and made rapid progresses in the past years. However, many challenges pose on the SE4AR due to the specific features and complexities of autonomous robot software (ARS) and several open problems should be tackled in the future researches. This paper aims to present a comprehensive investigation on the researches and practices of SE4AR. Our contributions are three-fold as follows: an in-depth analysis on the development challenges, a systematic review on the current progresses, and an open discussion of weaknesses in current researches and opportunities in future researches.
Assessing Language Identification over DBpedia

Multilingual knowledge bases are the key for cross-lingual and multilingual applications such as Question Answering, Machine Translation, and Search. They facilitate information access and provide valuable training corpora for multi- and cross-lingual methods and frameworks. In this work, we evaluate the performance of different language identification methods and frameworks over DBpedia. We show that these approaches perform poorly on rdfs:labels.
Fusing Visual Features and Metadata to Detect Flooding in Flickr Images

Social media platforms such as Flickr have become a source of information for the assessment of natural disasters, for instance assisting in flood mapping. Visual features and textual metadata have been used to identify natural disasters in social media images, however, they have often been used separately. Here, we fuse these two modes together using two fusion methods and deep learning to identify flood images in the MediaEval 2017 dataset. A novel backpropagation technique, Direct Backpropagation (DBP) is used to train a neural network for the classification. The results show that the fusion methods improve the classification accuracy compared to their individual counterparts. We compare our proposed learning method with other baseline methods and find it producing highest classification results. For external evaluation, the results are compared with MediaEval 2017 methods, where our methods outperform most of them.
Non-autoregressive Deliberation-Attention based End-to-End ASR

Attention-based encoder-decoder end-to-end (E2E) automatic speech recognition (ASR) architectures have achieved the state-of-the-art results on many ASR tasks. However, the conventional attention-based E2E ASR models rely on the autoregressive decoder, which makes the parallel computation in decoding difficult. In this paper, we propose a novel deliberation-attention (D-Att) based E2E ASR architecture, which re-places the autoregressive attention-based decoder with the non-autoregressive frame level D-Att decoder, and thus accelerates the GPU parallel decoding speed significantly. D-Att decoder differs from the conventional attention decoder on two aspects: first, D-Att decoder uses the frame level text embedding (FLTE) generated by an auxiliary ASR model instead of the ground truth transcripts or previous predictions which are required by the conventional attention decoder; second, conventional attention decoder is trained in the left-to-right label-synchronous way, however, D-Att decoder is trained under the supervision of connectionist temporal classification (CTC) loss and utilizes the FLTE to provide the text information. Our experiments on Aishell, HKUST and WSJ benchmarks show that the proposed D-Att E2E ASR models are comparable to the performance of the state-of-the-art autoregressive attention-based transformer E2E ASR baselines, and are 10 times faster with GPU parallel decoding.