Reviewerly/exHarmony huggingface dataset:
Iteration Time Prediction for CNN in Multi-GPU Platform: Modeling and Analysis

Neural networks, as powerful models for many difficult learning tasks, have created an increasingly heavy computational burden. More and more researchers focus on how to optimize the training time, and one of the difficulties is to establish a general iteration time prediction model. However, the existing models have high complexity or tedious build processes, and there is still space for improvement in prediction accuracy. Moreover, there is little systematic analysis of multi-GPU which is a special and widely used scenario. In this paper, we introduce a framework to analyze the training time for convolutional neural networks (CNNs) on multi-GPU platforms. Based on the analysis of GPU calculation principles and its special transmission mode, our framework decomposes the model and obtain accurate prediction results without long-term training or complex data collection. We start by extracting key feature parameters related to GPUs, CNNs, and networks. Then, we map CNN architectures to constraints, including software platforms, GPU platforms, parallel strategies, and communication strategies. At last, we provide the prediction model and give analysis results of training time from multiple perspectives. The proposed model is verified on four types of NVIDIA GPU platforms and six different CNN architectures. The experiment results show that the average error across varies scenarios is less than 15% and outperform the state-of-the-art results by 5%-30%, which corroborate our model an effective tool for artificial intelligence (AI) researchers.
ZARA

Generative Adversarial Networks (GANs) recently demonstrated a great opportunity toward unsupervised learning with the intention to mitigate the massive human efforts on data labeling in supervised learning algorithms. GAN combines a generative model and a discriminative model to oppose each other in an adversarial situation to refine their abilities. Existing nonvolatile memory based machine learning accelerators, however, could not support the computational needs required by GAN training. Specifically, the generator utilizes a new operator, called transposed convolution, which introduces significant resource underutilization when executed on conventional neural network accelerators as it inserts massive zeros in its input before a convolution operation. In this work, we propose a novel computational deformation technique that synergistically optimizes the forward and backward functions in transposed convolution to eliminate the large resource underutilization. In addition, we present dedicated control units - a dataflow mapper and an operation scheduler, to support the proposed execution model with high parallelism and low energy consumption. ZARA is implemented with commodity ReRAM chips, and experimental results show that our design can improve GAN's training performance by averagely 1.6× ~23× over CMOS-based GAN accelerators. Compared to state-of-the-art ReRAM-based accelerator designs, ZARA also provides 1.15 × ~2.1× performance improvement.
Detecting small faces in the wild based on generative adversarial network and contextual information

Face detection techniques have been developed for decades, and one of the remaining open challenges is detecting small faces in unconstrained conditions. The reason is that tiny faces are often lacking detailed information and blurry. In this paper, we proposed an algorithm to directly generate a clear high-resolution face from a small blurry one by adopting a generative adversarial network (GAN). Toward this end, the basic GAN formulation achieves it by super-resolving and refining sequentially (e.g. SR-GAN and Cycle-GAN). However, we design a novel network to address the problem of super-resolving and refining jointly. Moreover, we also introduce new training losses (i.e. classification loss and regression loss) to promote the generator network to recover fine details of the small faces and to guide the discriminator network to distinguish face vs. non-face and to refine location simultaneously. Additionally, considering the importance of contextual information when detecting tiny faces in crowded cases, the context around face regions is combined to train the proposed GAN-based network for mining those very small faces from unconstrained scenarios. Extensive experiments on the challenging datasets WIDER FACE and FDDB demonstrate the effectiveness of the proposed method in restoring a clear high-resolution face from a small blurry one, and show that the achieved performance outperforms previous state-of-the-art methods by a large margin.
Sentiment Analysis Techniques and Applications in Education: A Survey

As the interplay between cognition and emotion is involved in every learning process, student profile should be enhanced with information regarding his/her affective state. Sentiment analysis could serve this end, through the analysis of student behavioral traces in teaching-learning environments. The purpose of the present study is to review the status of research on the field of sentiment analysis in the educational domain; exploring different ways in which sentiment analysis has been applied in the educational domain, and analyze the different techniques that researchers have adopted in developing sentiment analysis systems on educational datasets. Five different task types that sentiment analysis has served within the domain were identified, namely: (i) instruction evaluation, (ii) institutional decision/policy making, (iii) intelligent information/learning systems enhancement, (iv) assignment evaluation and feedback improvement, and (v) new research insights. From a technical perspective, a brief explanation of the different sentiment analysis techniques along with representative examples are presented. The character of this work may address the needs of a diverse group of stakeholders, including educators, social sciences researchers as well as researchers, in natural language processing in education.
Thermal quantum correlations in a two-dimensional spin star model

In this paper, we study the thermal evolution of three types of quantum correlations under the homogeneous and inhomogeneous spin star Hamiltonian. It is shown that quantum discord (QD) is more stable than the other measures in the thermal regime, but concurrence is more efficient when the Hamiltonian parameters are employed. However, all quantum correlations can reach their maximum, if the inhomogeneous parameter raises. Quantum correlations can be enhanced by a weak external magnetic field and strong coupling parameter. But they vanish in the case of a strong magnetic field, weak coupling parameter, and high temperatures.
A trust management framework for clouds

In today’s cloud computing platforms, more and more users are now working or collaborating in the multi-cloud environment, in which collaborators, clouds, computing nodes may belong to different institutions or organizations. Those different organizations might have their policies. Security is still a big concern in cloud computing. To help cloud vendors and customers to detect and prevent from being affected by potential attacks, we propose a trust management framework. We consider link/flow’s level trust, node’s level trust, and task/mission’s level trust. We introduced a new security metric trustability (trust–reliability) and a new algorithm to calculate it. Trustability measures how much a system can be trusted under a specific attack vector. Trustability can be used to explore the design space of resource configuration in order be able to choose the right trade-off between trustability and cost of redundancy. We show that our trust management framework can guide the administrators and customers to make decisions. For example, based on the real-time trust information, cloud administrators can migrate tasks from suspect nodes to trustworthy nodes, dynamically allocate a resource, and manage the trade-off between the degree of redundancy and the cost of the resource.
Episodic use: Practices of care in self-tracking

The development of self-tracking technologies has resulted in a burst of research considering how self-tracking practices manifest themselves in everyday life. Based on a 5-month-long photo elicitation study of Danish self-trackers, we argue that no matter how committed people might be to tracking their activities, their use of self-tracking technologies can be best described as episodic rather than continuous. Using Annemarie Mol’s theoretical framework for understanding care practices as a lens, we show how episodic use can be interpreted through the logic of care. By using self-tracking devices episodically, users employ strategies of care in a way that can be productive and useful. These strategies often come in conflict with the logics of choice that underlie the design of many self-tracking technologies. We argue that this has consequences for the way self-tracking devices need to be imagined, designed, and introduced as part of workplace and insurance-type tracking programs.
Use of Data Mining Methods to Detect Test Fraud

Abstract Data mining methods have drawn considerable attention across diverse scientific fields. However, few applications could be found in the areas of psychological and educational measurement, and particularly pertinent to this article, in test security research. In this study, various data mining methods for detecting cheating behaviors on large‐scale assessments are explored as an alternative to the traditional methods including person‐fit statistics and similarity analysis. A common data set from the Handbook of Quantitative Methods for Detecting Cheating on Tests (Cizek &amp; Wollack) was used for comparing the performance of the different methods. The results indicated that the use of data mining methods may combine multiple sources of information about test takers' performance, which may lead to higher detection rate over traditional item response and response time methods. Several recommendations, all based on our findings, are provided to practitioners.
Deep Progressive Hashing for Image Retrieval

Hashing is a widely adopted method based on an approximate nearest neighbor search and is used in large-scale image retrieval tasks. Conventional learning-based hashing algorithms employ end-to-end representation learning, which is a one-off technique. Because of the tradeoff between efficiency and performance, conventional learning-based hashing methods must sacrifice code length to improve performance, which increases their computational complexity. To improve the efficiency of binary codes, motivated by the “nonsalient-to-salient” attention scheme of humans, we propose a recursive hashing mechanism that maps progressively expanded salient regions to a series of binary codes. These salient regions are generated by a conventional saliency model based on bottom-up saliency-driven attention and a semantic-guided saliency model based on top-down task-driven attention. After obtaining a series of salient regions, we perform long-range temporal modeling of salient regions using a graph-based recurrent deep network to obtain more refined representative features. The later output nodes inherit aggregated information from all previous nodes and extract discriminative features from more salient regions. Therefore, this network possesses more significant information and satisfactory scalability. The proposed recursive hashing neural network, optimized by a triplet ranking loss, is end-to-end trainable. Extensive experimental results from several image retrieval benchmarks show the scalability of our method and demonstrate its strong performance compared with state-of-the-art methods.
Decentralized Efficiency? Arbitrage in Bitcoin Markets

Using tick-level bitcoin data from February 2013 through April 2018, we show substantial arbitrage spreads between global bitcoin markets. Spreads follow multiple consistent patterns. Minimum and maximum prices show significant clustering. Spreads increase during the early hours of a day (according to coordinated universal time), when new exchanges enter markets, and following bitcoin heists and hacks. The full year 2017 and the first quarter of 2018 each had exploitable net arbitrage profit opportunities of at least USD380 million that smart money failed to capture. Based on long-term analyses, we also found that bitcoin market inefficiency has increased over time.
Towards genre-specific frameworks for video summarisation: A survey

Video summarisation is characterised as the process of extracting meaningful frames or segments from a video that best represents the content of the whole video. The proposed framework surveys and categorizes the existing video summarisation models in the recent research works on the basis of genre. The most important phase of video summarisation is the detection of key frames or segments in the video. The strategy for identifying key frames or segments vary for each genre. The various genre analysed are user generated videos, movies and documentary, sports, surveillance, egocentric and informational talk videos with a total of more than 25 varying parameters significant to the respective genre. Comprehensive evaluations of the results obtained from the models are also included based on quantitative and qualitative parameters. The framework will help the user in deciding the technology to be adopted for video summarisation in a particular domain. The framework also aids in deciding the type of summary suitable for each genre and the available datasets in each genre for experimental analysis.
Optimal Freshness Crawl Under Politeness Constraints

A Web crawler is an essential part of a search engine that procures information subsequently served by the search engine to its users. As the Web is becoming increasingly more dynamic, in addition to discovering new web pages a crawler needs to keep revisiting those already in the search engine's index, in order to keep the index fresh by picking up the pages' changed content. Determining how often to recrawl pages requires making tradeoffs based on the pages' relative importance and change rates, subject to multiple resource constraints - the limited daily budget of crawl requests on the search engine's end and politeness constraints restricting the rate at which pages can be requested from a given host. In this paper, we introduce PoliteBinaryLambdaCrawl, the first optimal algorithm for freshness crawl scheduling in the presence of politeness constraints as well as non-uniform page importance scores and the crawler's own crawl request limit. We also propose an approximation for it, stating its theoretical optimality conditions and in the process discovering a connection to an approach previously thought of as a mere heuristic for freshness crawl scheduling. We explore the relative performance of PoliteBinaryLambdaCrawl and other methods for handling politeness constraints on a dataset collected by crawling over 18.5M URLs daily over 14 weeks.
Hybrid particle swarm optimization-genetic algorithm trained multi-layer perceptron for classification of human glioma from molecular brain neoplasia data

Multi-Layer Perceptron (MLP) is among the most widely applied Artificial Neural Networks (ANNs). Multi-Layer Perceptron (MLP) requires specific designing and training depending upon specific applications. This paper deals with the high-dimensional problem of classification of human glioma from Molecular Human Brain Neoplasia Data by designing a Multi-Layer Perceptron (MLP) which is trained through hybridizing Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The results are compared with individual algorithms in terms of convergence rate, Mean Squared Error (MSE) and classification accuracy.
Medical image segmentation algorithm based on feedback mechanism convolutional neural network

The traditional image segmentation method relies on artificial means to extract and select information such as edges, colors and textures in the image. It not only consumes a lot of time and effort, but also requires certain expertise to obtain useful feature information which no longer meets the practical application requirements of medical image segmentation and recognition. As an efficient image segmentation method, Convolutional Neural Networks (CNNs) have been widely promoted and applied in the field of medical image segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid development of the medical field. Therefore, inspired by the feedback mechanism of human visual cortex, we give a deep research on how to build a computational model of feedback mechanism in deep convolutional neural networks. And effective feedback mechanism calculation models and operation frameworks are proposed. In this paper, to solve the feedback optimization problem, we propose two new algorithms based on the greedy strategy. We analyze the functional differences of these two algorithms, and propose a new feedback convolutional neural network algorithm based on the neuron pathway pruning and pattern information recovering algorithms. For the problem that it is difficult to find and extract effective features in medical image segmentation, we propose a medical image segmentation algorithm based on feedback mechanism convolutional neural network. The basic idea is as follows. Firstly, using the unlabeled image block sample training, learning and extracting the deep features of the image to construct feedback mechanism convolutional neural network models. Then, using the model to classify the pixel block samples in the medical image to be segmented, and the initial regions of the image is obtained. Finally, the initial results are optimized by threshold segmentation and morphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation method not only has high segmentation accuracy, but also has extremely high adaptive segmentation ability for various medical images.
Blue-Noise Sampling on Graphs

In the area of graph signal processing, a graph is a set of nodes arbitrarily connected by weighted links; a graph signal is a set of scalar values associated with each node; and sampling is the problem of selecting an optimal subset of nodes from which a graph signal can be reconstructed. This paper proposes the use of spatial dithering on the vertex domain of the graph, as a way to conveniently find statistically good sampling sets. This is done establishing that there is a family of good sampling sets characterized on the vertex domain by a maximization of the distance between sampling nodes; in the Fourier domain, these are characterized by spectrums that are dominated by high frequencies referred to as blue-noise. The theoretical connection between blue-noise sampling on graphs and previous results in graph signal processing is also established, explaining the advantages of the proposed approach. Restricting our analysis to undirected and connected graphs, numerical tests are performed in order to compare the effectiveness of blue-noise sampling against other approaches.
RotationNet for Joint Object Categorization and Unsupervised Pose Estimation from Multi-View Images

We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known viewpoint labels for training, our method treats the viewpoint labels as latent variables, which are learned in an unsupervised manner during the training using an unaligned object dataset. RotationNet uses only a partial set of multi-view images for inference, and this property makes it useful in practical scenarios where only partial views are available. Moreover, our pose alignment strategy enables one to obtain view-specific feature representations shared across classes, which is important to maintain high accuracy in both object categorization and pose estimation. Effectiveness of RotationNet is demonstrated by its superior performance to the state-of-the-art methods of 3D object classification on 10- and 40-class ModelNet datasets. We also show that RotationNet, even trained without known poses, achieves comparable performance to the state-of-the-art methods on an object pose estimation dataset. Furthermore, our object ranking method based on classification by RotationNet achieved the first prize in two tracks of the 3D Shape Retrieval Contest (SHREC) 2017. Finally, we demonstrate the performance of real-world applications of RotationNet trained with our newly created multi-view image dataset using a moving USB camera.
Privacy Techniques for Edge Computing Systems

In an edge-enabled data management and computing environment, it is critical to ensure the privacy of the information acquired, processed, and exchanged among the different parties. The problem is complex because of the large scale, mobility, device, and protocol heterogeneity. Also, unlike in conventional environments, communication may be fragmented and portions of the environment can be physically unprotected. To date, there are several privacy-enhancing techniques, such as secure multiparty computation techniques, private information retrieval (PIR), and data sanitization techniques. However, there is not a single technique that works for all possible uses of the data in edge systems. In addition, these techniques are computationally expensive and thus may not be suitable for edge devices. In this paper, we first cover basic privacy building blocks, including differential privacy and homomorphic encryption. We then discuss privacy solutions specific to three different types of data use that are relevant for edge-based applications: data aggregation techniques, point-of-interest (POI) services and traffic information services, and crowdsourcing. These applications have been selected as they provide a broad spectrum of edge computing applications. Throughout this paper, we outline open research directions.
Computational analysis of Bayer colour filter arrays and demosaicking algorithms in digital photoelasticity

Digital photoelasticity often evaluates the stress fields in loaded structures by processing the colour intensities recorded in digital images. This makes the evaluation process sensitive to the accuracy of the colours recorded by cameras. This paper analyses errors introduced by cameras with Bayer colour filter arrays and demosaicking algorithms in the photoelasticity images, and consequently, in the stress recovered information. The results provide significant evidence suggesting that photoelasticity studies should pay attention to the effects of colour filter arrays and demosaicking algorithms because they introduce a variety of errors when evaluating the stress due to different experimental conditions, such as light sources, camera sensors, load applications, image resolution, geometry, and polariscope configurations.