Reviewerly/exHarmony huggingface dataset:
Key factors in the implementation of Cloud Computing as a service and communication tool in universities

Some universities still do not offer Cloud Computing as a service and communication tool in their services or their degree of implementation is low, both at an administrative level and at a teaching level, despite its advantages and the fact that this technology began to be implemented in these institutions many years ago. Thus, this study seeks to find, through a bibliographic study and a causal study carried out by experts in technologies and technological implementation, which are the factors responsible for this, the determining factors in the decision of universities to accept and implement Cloud Computing. The list obtained is made up of the following factors (in order of relevance in this decision): security, pressure from administration staff, professors and students, support from university management, relative advantage, support from providers Information Systems (IS), government incentives, compatibility, organizational technological readiness, perceived reliability of technology, government pressure, characteristics of rectors, pressure from competitors (other universities), size of universities, complexity and perceived cost.
Digital Assembly Assistance System in Industry 4.0 Era: A Case Study with Projected Augmented Reality

Automation has increased more and more in manufacturing companies over the last decades. However, manual labor is still used in a variety of complex tasks and is currently irreplaceable, especially in assembly operations. Assisting and supporting the human worker during potentially complex assembly tasks is very relevant. Clear and easy-to-read assembly instructions, error-proofing methods, and an intuitive user interface for the worker have the potential to not only reduce the cognitive workload of the operator but also increase the productivity, improve the quality, reduce defects, and consequently reduce costs. Industry 4.0 technologies, in particular Augmented Reality and motion recognition sensors, can help companies in reaching these goals. However, there are currently only a few works that show how to implement these technologies with real case studies, especially with the Projected Augmented Reality (PAR). This is the reason why, in this paper an example of prototype of a smart workstation equipped with a Kinect-projector assistance system for manual assembly is presented.
Improving Software Defect Prediction by Aggregated Change Metrics

To ensure the delivery of high quality software, it is necessary to ensure that all of its artifacts function properly, which is usually done by performing appropriate tests with limited resources. It is therefore desirable to identify defective artifacts so that they can be corrected before the testing process. So far, researchers have proposed various predictive models for this purpose. Such models are typically trained on data representing previous project versions of a software and then used to predict which of the software artifacts in the new version are likely to be defective. However, the data representing a software project usually consists of measurable properties of the project or its modules, and leaves out information about the timeline of the software development process. To fill this gap, we propose a new set of metrics, namely aggregated change metrics, which are created by aggregating the data of all changes made to the software between two versions, taking into account the chronological order of the changes. In experiments conducted on open source projects written in Java, we show that the stability and performance of commonly used classification models are improved by extending a feature set to include both measurable properties of the analyzed software and the aggregated change metrics.
Immersive Virtual Reality and Complex Skill Learning: Transfer Effects After Training in Younger and Older Adults

This study investigated the efficacy of training a complex skill within an immersive virtual environment. We constructed a sensorimotor workstation task and trained participants to operate it with either a non-interactive (text, video) method or with a virtual equivalent of the workstation. Our results indicate that virtual reality trained participants had significantly higher accuracy and produced fewer timeouts in the workstation task than those trained using non-interactive methods, demonstrating a successful transfer of skills between virtual reality and the real world. Further, comparing younger and older adults in their performance, we found that older adults did not differ in their ability to benefit from virtual reality training, and that the extent of this benefit was not significantly affected by existing cognitive deficits (as measured using the Operation Span and AX-Continuous Performance tasks). These findings suggest that virtual reality technologies can find application in a wide range of skill training contexts and across diverse age groups.
Multilingual Sentiment Analysis: A Systematic Literature Review

With the explosive growth of social media, the online community can freely express their opinions without disclosing their identities. People with hidden agendas can easily post fake opinions to discredit target products, services, politicians, or organizations. With these big data, monitoring opinions and distilling their sentiments remain a formidable task because of the proliferation of diverse sites with a large volume of opinions that are portrayed in multilingual. Therefore, this paper aims to provide a systematic literature review on multilingual sentiment analysis, which summarises the common languages supported in multilingual sentiment analysis, pre-processing techniques, existing sentiment analysis approaches, and evaluation models that have been used for multilingual sentiment analysis. By following the systematic literature review, the findings revealed, most of the models supported two languages, and English is seen as the most used language in sentiment analysis studies. None of the reviewed literature has catered the combination of languages for English, Chinese, Malay, and Hindi language on multilingual sentiment analysis. The common pre-processing techniques for the multilingual domain are tokenization, normalization, capitalization, N-gram, and machine translation. Meanwhile, the sentiment analysis classification techniques for multilingual sentiment are hybrid sentiment analysis, which includes localized language analysis, unsupervised topic clustering, and then followed by multilingual sentiment analysis. In terms of evaluation, most of the studies used precision, recall, and accuracy as the benchmark for the results.
Direct estimation of the energy gap between the ground state and excited state with quantum annealing

Abstract In quantum chemistry, it is important to estimate an energy gap between a ground state and an excited state of molecular Hamiltonians. In previous researches, it was necessary to measure the energy of the ground state and that of the excited state separately, and the energy gap was estimated from the subtraction between them. Here, we show a novel scheme to estimate such an energy gap in a more direct manner. We use a concept of a Ramsey type measurement in the quantum annealing (QA) for such a direct estimation of the energy gap. In our scheme, the measured signal oscillates with a frequency of the energy gap. To study the performance of our scheme, we perform numerical simulations. The results show our scheme is robust against non-adiabatic transitions between the ground state and first excited state. Our scheme paves the way for the practical application of QA in quantum chemistry.
DistPrivacy: Privacy-Aware Distributed Deep Neural Networks in IoT surveillance systems

With the emergence of smart cities, Internet of Things (IoT) devices as well as deep learning technologies have witnessed an increasing adoption. To support the requirements of such paradigm in terms of memory and computation, joint and real-time deep co-inference framework with IoT synergy was introduced. However, the distribution of Deep Neural Networks (DNN) has drawn attention to the privacy protection of sensitive data. In this context, various threats have been presented, including black-box attacks, where a malicious participant can accurately recover an arbitrary input fed into his device. In this paper, we introduce a methodology aiming to secure the sensitive data through re-thinking the distribution strategy, without adding any computation overhead. First, we examine the characteristics of the model structure that make it susceptible to privacy threats. We found that the more we divide the model feature maps into a high number of devices, the better we hide proprieties of the original image. We formulate such a methodology, namely DistPrivacy, as an optimization problem, where we establish a trade-off between the latency of co-inference, the privacy level of the data, and the limited-resources of IoT participants. Due to the NP-hardness of the problem, we introduce an online heuristic that supports heterogeneous IoT devices as well as multiple DNNs and datasets, making the pervasive system a general-purpose platform for privacy-aware and low decision-latency applications.
SEAWARE: Semantic Aware View Prediction System for 360-degree Video Streaming

Future view prediction for a 360-degree video streaming system is important to save the network bandwidth and improve the Quality of Experience (QoE). Historical view data of a single viewer and multiple viewers have been used for future view prediction. Video semantic information is also useful to predict the viewer's future behavior. However, extracting video semantic information requires powerful computing hardware and large memory space to perform deep learning-based video analysis. It is not a desirable condition for most of client devices, such as small mobile devices or Head Mounted Display (HMD). Therefore, we develop an approach where video semantic analysis is executed on the media server, and the analysis results are shared with clients via the Semantic Flow Descriptor (SFD) and View-Object State Machine (VOSM). SFD and VOSM become new descriptive additions of the Media Presentation Description (MPD) and Spatial Relation Description (SRD) to support 360-degree video streaming. Using the semantic-based approach, we design the Semantic-Aware View Prediction System (SEAWARE) to improve the overall view prediction performance. The evaluation results of 360-degree videos and real HMD view traces show that the SEAWARE system improves the view prediction performance and streams high-quality video with limited network bandwidth.
Fair Outsourcing Polynomial Computation Based on the Blockchain

Due to the big data blowout from the Internet of Things and the rapid development of cloud computing, outsourcing computation has received considerable attention in recent years. Particularly, many outsourcing computation schemes have been proposed to dedicate the outsourcing polynomial computation due to its use in numerous fields, such as data analysis and machine learning. However, none of those schemes are practical enough, as they either require some time-consuming cryptographic operations to achieve fairness between the user and the worker, or cannot allow the user to outsource arbitrary polynomial to the worker, or need two non-collusive workers. To tackle these challenges, in this article, we propose a new outsourcing polynomial computation scheme by employing a variant of Horner’s method and the blockchain technology. Specifically, the former makes the computational cost on the worker side as low as possible, and the latter guarantees the fairness between the user and the worker if the result from the worker can be publicly verified. To achieve the public verifiability property, we apply the sampling technique, which is effective in our proposal according to a game-theoretic analysis. Furthermore, we also implement a prototype of our proposal and run it on an Ethereum test net. The extensive experimental results demonstrate that our proposal is efficient in terms of computational cost.
Adversarial Training for Solving Inverse Problems in Image Processing

Inverse problems are a group of important mathematical problems that aim at estimating source data x and operation parameters z from inadequate observations y. In the image processing field, most recent deep learning-based methods simply deal with such problems under a pixel-wise regression framework (from y to x) while ignoring the physics behind. In this paper, we re-examine these problems under a different viewpoint and propose a novel framework for solving certain types of inverse problems in image processing. Instead of predicting x directly from y, we train a deep neural network to estimate the degradation parameters z under an adversarial training paradigm. We show that if the degradation behind satisfies some certain assumptions, the solution to the problem can be improved by introducing additional adversarial constraints to the parameter space and the training may not even require pair-wise supervision. In our experiment, we apply our method to a variety of real-world problems, including image denoising, image deraining, image shadow removal, non-uniform illumination correction, and underdetermined blind source separation of images or speech signals. The results on multiple tasks demonstrate the effectiveness of our method.
Product Recommendation System a Comprehensive Review

Abstract In today’s technical era, every startup or a company attempt to establish a better sort of communication between their products and the users, and for that purpose, they require a type of mechanism which can promote their product effectively, and here the recommender system serves this motive. It is basically a filtering system that tries to predict and show the items that a user would like to purchase. By analyzing the preference of the users, companies can decide which product to be launched in the market to procure more benefits. These systems are proved to be very beneficial in variety of domains involving music, books, movies, research articles and products in common. In this paper, we review various mechanisms and techniques that are required for recommender systems for recommending the products or items in the domain of fashion and books.
A Survey on Data-driven Performance Tuning for Big Data Analytics Platforms

Many research works deal with big data platforms looking forward to data science and analytics. These are complex and usually distributed environments, composed of several systems and tools. As expected, there is a need for a closer look at performance issues. In this work, we review performance tuning strategies in the big data environment. We focus on data-driven tuning techniques, discussing the use of database inspired approaches. Concerning big data and NoSQL stores, performance tuning issues are quite different from the so-called conventional systems. Many existing solutions are mostly ad-hoc activities that do not fit for multiple situations. But there are some categories of data-driven solutions that can be taken as guidelines and incorporated into general-purpose auto-tuning modules for big data systems. We examine typical performance tuning actions, discussing available solutions to support some of the tuning process's primary activities. We also discuss recent implementations of data-driven performance tuning solutions for big data platforms. We propose an initial classification based on the domain state-of-the-art and present selected tuning actions for large-scale data processing systems. Finally, we organized existing works towards self-tuning big data systems based on this classification and presented general and system-specific tuning recommendations. We found that most of the literature pieces evaluate the use of tuning actions at the physical design perspective, and there is a lack of self-tuning machine-learning-based solutions for big data systems.
Analysis and Computation of Encryption Technique to Enhance Security of Medical Images

Abstract Recently, there are many advanced techniques used in the medical fields, such as smart health, e-health and telemedicine applications. These techniques rely on open source networks to transmit the digital medical images. These images have very sensitive and confidential information about the patients. Regrettably, most of the regularly used algorithms provide very less security and present a high communication overhead, with high computation costs. Due to these issues, a Region of Interest (ROI) method, based on selective image encryption, is proposed in this paper. An active contour image segmentation is first used to divide the ROI and the Region of Background (ROB). Then, a Hilbert curve, with a Skew Tent map are used to implement the Permutation and diffusion techniques. Namely, the ROI part is permuted according to a Hilbert scan pattern, to decrease the similarities between the inside adjacent pixels. Then pixels in the permuted ROI part are XOR pixel-wise with random numbers, generated based on a predefined threshold value from a Skew Tent map. Finally, encrypted ROI and ROB blocks are combined to generate encrypted images. Given the obtained experimental results, the proposed method is very likely to improve image security, through the employment of correlation checking, key sensitivity, entropy, diffusion characteristic and histogram tools.
Vision-Based Fall Detection Using Dense Block With Multi-Channel Convolutional Fusion Strategy

Fall detection has become a hot issue in the field of video surveillance recently. Different from most traditional vision-based methods relying on hand-crafted features, fall detection methods based on deep learning technology can automatically mine features to detect fall events due to the powerful ability of deep learning in data analysis, and hence have received much more attention in recent years. However, information loss has become a problem that cannot be ignored, especially for the neural networks with deep layers, because loss of information will affect representativeness of features and further influence the performance of fall detection. To solve the abovementioned problem, we propose a fall detection method based on dense block with a multi-channel convolutional fusion (MCCF) strategy. In this method, MCCF-DenseBlock, a densely connected network structure, is proposed to fully extract information with its densely connected layers, and to avoid network overloading by breaking dense connections appropriately, and especially to reduce data redundancy and numerous parameters in the network via the MCCF strategy fusing its grouped features. Besides, an improved transition layer is presented to further lessen data accumulation by using a multi-level downsampling structure. Experimental results demonstrate that, the proposed method can provide accurate fall detection results (satisfactory F-score of 0.973 on the UR Fall Dataset) and outperforms several state-of-the-art methods.
Emotion Detection and Sentiment Analysis of Static Images

The usage of social media platform such as Facebook, Instagram, Flicker, etc. is rising day by day wherein images play a major role. It is said “An image is worth a thousand words”, people these days upload certain images on these sites to display their sentiments and emotions in the form of picture on almost every occasion. Images play the most important role in today's generation where it has become a major part of everyone's lives. Most of the prevailing research have focused on sentiment analyses of textual data, but only limited researches have focused on analyzing sentiment of visual data. In this project, we have explored the possibilities of Convolutional Neural Networks (CNN) to predict the various emotions (happiness, surprise, sadness, fear, anger and neutral) depicted by an image. These sort of predictions can be useful in applications for automatic tag predictions of the visual data available on social media platforms and understanding sentiments of the people and their emotions.
Salient object detection by robust foreground and background seed selection

This paper presents a novel background and foreground seed selection method for graph-based salient object detection. First, according to the boundary prior which considers that the image boundary is mainly the background, we select the initial background seed set and optimize it through our proposed two-stage background seed correction processes by combing multiple internal image features. Second, different from most existing center-based prior methods, this paper uses the convex hull of the point of interest to estimate the location of salient objects and thus obtains the initial foreground seed set. Moreover, foreground seeds are refined by the proposed foreground seed correction process, which depends on the color and spatial differences between seeds. Third, we adopt the extended random walk to propagate the background and foreground labels. Finally, a fusion model is proposed to integrate the background- and foreground-based saliency maps, generating final salient object detection results. Experiments on publicly available data sets show that the proposed algorithm achieves better results in contrast to other state-of-the-art methods.
Conditional Generative ConvNets for Exemplar-Based Texture Synthesis

The goal of exemplar-based texture synthesis is to generate texture images that are visually similar to a given exemplar. Recently, promising results have been reported by methods relying on convolutional neural networks (ConvNets) pretrained on large-scale image datasets. However, these methods have difficulties in synthesizing image textures with non-local structures and extending to dynamic or sound textures. In this article, we present a conditional generative ConvNet (cgCNN) model which combines deep statistics and the probabilistic framework of generative ConvNet (gCNN) model. Given a texture exemplar, cgCNN defines a conditional distribution using deep statistics of a ConvNet, and synthesizes new textures by sampling from the conditional distribution. In contrast to previous deep texture models, the proposed cgCNN does not rely on pre-trained ConvNets but learns the weights of ConvNets for each input exemplar instead. As a result, cgCNN can synthesize high quality dynamic, sound and image textures in a unified manner. We also explore the theoretical connections between our model and other texture models. Further investigations show that the cgCNN model can be easily generalized to texture expansion and inpainting. Extensive experiments demonstrate that our model can achieve better or at least comparable results than the state-of-the-art methods.
Data Poisoning Attacks on Regression Learning and Corresponding Defenses

Adversarial data poisoning is an effective attack against machine learning and threatens model integrity by introducing poisoned data into the training dataset. So far, it has been studied mostly for classification, even though regression learning is used in many mission critical systems (such as dosage of medication, control of cyber-physical systems and managing power supply). Therefore, in the present research, we aim to evaluate all aspects of data poisoning attacks on regression learning, exceeding previous work both in terms of breadth and depth. We present realistic scenarios in which data poisoning attacks threaten production systems and introduce a novel black-box attack, which is then applied to a real-word medical use-case. As a result, we observe that the mean squared error (MSE) of the regressor increases to 150 percent due to inserting only two percent of poison samples. Finally, we present a new defense strategy against the novel and previous attacks and evaluate it thoroughly on 26 datasets. As a result of the conducted experiments, we conclude that the proposed defence strategy effectively mitigates the considered attacks.