Reviewerly/exHarmony huggingface dataset:
Frequency Improvement of Systolic Array-Based CNNs on FPGAs

FPGAs are commercially available off-the-shelf for implementing convolutional neural network (CNN) accelerators to trade off accuracy, performance, and power. Systolic array architecture for CNN accelerators on FPGAs has the potential to run at a high frequency due to its regular and simple interconnections. However, current FPGA CAD tools are unable to synthesize and layout systolic arrays in high quality. In this paper, we identify the reasons for the frequency degradation of systolic array designs for CNN accelerators. We also propose two methods to improve the frequency at the front-end and the back-end, respectively. The experimental results show that our methods are able to achieve 1.29 × higher frequency and attain 1.5TOPS for the VGG16 network on the Xilinx KCU1500 platform.
Matrix Product State–Based Quantum Classifier

Interest in quantum computing has increased significantly. Tensor network theory has become increasingly popular and widely used to simulate strongly entangled correlated systems. Matrix product state (MPS) is a well-designed class of tensor network states that plays an important role in processing quantum information. In this letter, we show that MPS, as a one-dimensional array of tensors, can be used to classify classical and quantum data. We have performed binary classification of the classical machine learning data set Iris encoded in a quantum state. We have also investigated its performance by considering different parameters on the ibmqx4 quantum computer and proved that MPS circuits can be used to attain better accuracy. Furthermore the learning ability of an MPS quantum classifier is tested to classify evapotranspiration (ET[Formula: see text]) for the Patiala meteorological station located in northern Punjab (India), using three years of a historical data set (Agri). We have used different performance metrics of classification to measure its capability. Finally, the results are plotted and the degree of correspondence among values of each sample is shown.
VirtCo: joint coflow scheduling and virtual machine placement in cloud data centers

Cloud data centers, such as Amazon EC2, host myriad big data applications using Virtual Machines (VMs).As these applications are communication-intensive, optimizing network transfer between VMs is critical to the performance of these applications and network utilization of data centers.Previous studies have addressed this issue by scheduling network flows with coflow semantics or optimizing VM placement with traffic considerations.However, coflow scheduling and VM placement have been conducted orthogonally.In fact, these two mechanisms are mutually dependent, and optimizing these two complementary degrees of freedom independently turns out to be suboptimal.In this paper, we present VirtCO, a practical framework that jointly schedules coflows and places VMs ahead of VM launch to optimize the overall performance of data center applications.We model the joint coflow scheduling and VM placement optimization problem, and propose effective heuristics for solving it.We further implement VirtCO with OpenStack and deploy it in a testbed environment.Extensive evaluation of real-world traces shows that compared with state-of-the-art solutions, VirtCO greatly reduces the average coflow completion time by up to 36.5%.This new framework is also compatible with and readily deployable within existing data center architectures.
Face Expression Recognition with the Optimization based Multi-SVNN Classifier and the Modified LDP Features

Facial expression recognition (FER) is the interesting research area that enables us to recognize the expression of the human face in the day-to-day life. Most of the traditional methods fail to recognize the expressions accurately as the expressions are based on the movements of the parts in the human face. The paper proposes the effective method of FER using the proposed Whale- Grasshopper Optimization algorithm based Multi-Support Vector Neural Network (W-GOA-based MultiSVNN). The features from the facial image is extracted using the Scale-Invariant Feature Transform (SIFT) and the proposed Scatter Local Directional Pattern (SLDP). The extracted features are classified using the proposed classifier to recognize the expression of the face. The proposed method of facial recognition enhances the recognition accuracy. The experimentation of the proposed algorithm is performed using the databases, such as Cohn-Kanade AU-Coded Expression Database and The Japanese Female Facial Expression (JAFFE) Database. The proposed algorithm outperforms the existing methods in terms of the accuracy, TPR, and FPR and the values are found to be 0.96, 0.96, and 0.009, respectively.
MC360IQA: The Multi-Channel CNN for Blind 360-Degree Image Quality Assessment

In this paper, we present a multi-channel convolution neural network (CNN) for blind 360-degree image quality assessment (MC360IQA). To be consistent with the visual content of 360-degree images seen in the VR device, our model adopts the viewport images as the input. Specifically, we project each 360-degree image into six viewport images to cover omnidirectional visual content. By rotating the longitude of the front view, we can project one omnidirectional image onto lots of different groups of viewport images, which is an efficient way to avoid overfitting. MC360IQA consists of two parts, multi-channel CNN and image quality regressor. Multi-channel CNN includes six parallel ResNet34 networks, which are used to extract the features of the corresponding six viewport images. Image quality regressor fuses the features and regresses them to final scores. The results show that our model achieves the best performance among the state-of-art full-reference (FR) and no-reference (NR) image quality assessment (IQA) models on the available 360-degree IQA database.
Resource Conflict Checking and Resolution Controller Design for Cross-Organization Emergency Response Processes

A group of geographically dispersed and logically collaborated emergency organizations are involved when emergency occurs. Two challenging issues are a reasonable emergency resource allocation mechanism and an efficient resource conflict checking and resolution control mechanism to ensure the conflict-free execution of global cross-organization emergency response processes. To address them, this paper proposes an approach to support emergency resource management including both intraorganization private resource management and cross-organization public resource management. The former has been fully discussed in our previous work using E-net. To cope with the latter, this paper presents a novel type of Petri net that is extended with both time and resource factors to model cross-organization emergency response processes. Then, according to the resource requirement analysis, their worst, delayed, and best cases are obtained. Next, emergency resource conflict checking and four conflict resolution strategies are proposed to resolve the detected resource conflicts for the delayed execution case. This paper shows how to use different resolution strategies to construct the conflict-free model by designing corresponding resolution controllers. Finally, their performance is evaluated by a fire emergency response example.
Visual analysis of socio-cognitive crowd behaviors for surveillance: A survey and categorization of trends and methods

Monitoring and inferring socio-cognitive behaviors through crowd analysis can help us to understand many processes. Be it people in crowded environments, road traffic or even a flock of fish, situational awareness becomes critical for creating adequate disaster response, providing incident management, exercising control, etc. Recent researches have indicated that crowd modeling is conventionally based on density analysis. However, socio-cognitive behavior studies have demonstrated that crowds often display a wide variety of behaviors that arise spontaneously from the collective motions of unconnected individuals. Therefore, behavior analysis employing physics-based approaches only, thereby neglecting the socio-psychological aspects, may present diverse challenges to accurate inference. This means that by identifying and modeling some of the interacting agents that underpin the evolution of such behaviors, we can deliver contexts that can help in the autonomous analysis of social and antisocial behaviors in crowded environments. This paper discusses these issues from the machine vision perspective. In particular, socio-cognitive models of crowds are linked to low-level mechanisms of crowd modeling and feature extraction. A survey of recent works on crowd behavior analysis is conducted under a proposed behavioral categorization based on the level of the performed analysis and identified behaviors. Finally, discussions and recommendations are provided toward the advancement in the field.
A Cost-Effective Single-Shot Structured Light System for 3D Shape Measurement

Single-shot three-dimensional (3D) shape measurement techniques have attracted extensive researches, as they are suitable for dynamic measurement. In this study, a cost-effective single-shot structured light system (CES-SLS) is proposed for 3D shape measurement by using a color camera and a normal projector. With the aid of two planar mirrors and an isosceles right-angle mirror, a single color camera functions as a binocular vision. A single-shot color random speckle pattern (CRSP) is used to encode the object by the normal projector. The dense corresponding points (DCP) are derived by the spatial-temporal correlation of RGB image subsets instead of the spatial correlation of gray image subsets in the conventional method. An inverse compositional Gaussian Newton (IC-GN) iteration method with the second-order shape function is then introduced to find the sub-pixel corresponding points (CP). Furthermore, a geometrical 3D recovery method is presented to calculate the 3D point by minimizing the re-projection error. The experimental results demonstrate the comparative advantages of the proposed CES-SLS against the system using a single-shot random speckle pattern (RSP) and the curve fitting sub-pixel method in the aspects of measurement accuracy and noise robustness. In addition, the re-projection error of each 3D point from the geometrical method is smaller than that of the conventional mid-point.
Fall detection algorithm based on accelerometer and gyroscope sensor data using Recurrent Neural Networks

In our daily life activity, sometimes there is a chance of getting fall unintentionally. Unintentional falls are dangerous to health and may cause a serious problem, especially for elderly people whose have a higher probability of getting fall. In this paper, we develop an algorithm to distinguish falls from other activity daily living (ADL) based on accelerometer and gyroscope sensor data embedded on a wearable device. Several fall detection algorithms exist, with the majority are using rule-based algorithm. We take advantage of recurrent neural networks (RNN) as a tool for analyzing sequence time series data from sensors. The experiment was conducted using publicly available dataset UMA FALL ADL from Universidad de Málaga. The dataset consists of several recorded sensor-tag data, consisting of accelerometer, gyroscope and magnetometer sensor, representing the daily activity of several subjects including falls. Based on our experiment, we found that our algorithm yields a good result distinguishing fall from ADL.
Dimensionality reduction by collaborative preserving Fisher discriminant analysis

Sparse representation-based classifier (SRC) and collaborative representation-based classifier (CRC) are two commonly used classifiers. There has been pointed out that the utilization of all the training samples in representing a query sample (i.e. the least square part), which reflects the collaborative representation mechanism of SRC and CRC, is more important than the norm constraint on the coding coefficients for classification. From this perspective, both SRC and CRC can be viewed as collaborative representation (CR) but with different norm (i.e. L1 and L2) constraints on the coding coefficients. In this paper, two collaborative preserving Fisher discriminant analysis approaches are proposed for linear dimensionality reduction, in which both the local geometric information hidden in the CR coefficients and the global discriminant information inherited from Fisher/linear discriminant analysis (FDA/LDA) are effectively fused. Specifically, a datum adaptive graph is first built via CR with L1 or L2 norm constraint (corresponding to L1CPFDA and L2CPFDA, respectively), and then incorporated into the LDA framework to seek a powerful projection subspace with analytic solution. Both theoretical and experimental analysis of L1CPFDA and L2CPFDA show that they can best preserve the collaborative reconstruction relationship of the data and discriminate samples of different classes as well. Moreover, LDA is a special case of L1CPFDA and L2CPFDA and the available number of projection directions of them are twice that of LDA empirically. Experimental results on ORL, AR and FERET face databases and COIL-20 object database demonstrate their effectiveness, especially in low dimensions and small training sample size.
Quantum computing simulator on a heterogenous HPC system

Quantum computing simulation on a classical computer is difficult due to the exponential runtime and memory overhead. Previous work addresses the difficulty by utilizing multiple Graphical Processing Units (GPUs) and multi-node computers. GPUs are efficient for handling runtime issues but have limited total accessible memory space. Meanwhile, the memory of a multi-node computer can be scaled to the petabytes order, but its bandwidth for access from host computers (CPUs) is narrow. To simultaneously accelerate simulation and enlarge the total memory space, we propose a heterogeneous parallelization approach by combining GPUs and CPUs. Our simulator allocates memory to the GPUs first, and then to the CPUs. It thus accelerates simulation by using the full capabilities of the GPUs if memory for the simulation fits in the GPUs on a cluster. Allocating memory to the CPUs reduces benefits of the GPUs but enlarges the capacity of qubits in the simulation. In such case, it can exploit the memory of the GPUs to add one more qubit in the simulation if the size of memory in a node is the power of two (such as 512GB). We show empirical performance evaluations of our simulator in a distributed environment of POWER9.
Sentiment analysis through critic learning for optimizing convolutional neural networks with rules

Sentiment analysis is an important task in natural language processing. Previous studies have shown that integrating the knowledge rules into conventional classifiers can effectively improve the sentiment analysis accuracy. However, they suffer from two key deficiencies: (1) the given knowledge rules often contain mistakes or violations, which may hurt the performance if they cannot be adaptively utilized; (2) most of the studies leverage only the simple knowledge rules and sophisticated rules are ignored. In this paper, we propose a critic learning based convolutional neural network, which can address the two shortcomings. Our method is composed of three key parts, a feature-based predictor, a rule-based predictor and a critic learning network. The critic network can judge the importance of knowledge rules and adaptively use them. Moreover, a new filter initialization strategy is developed, which is able to take sophisticated rules into account. Extensive experiments are carried out, and the results show that the proposed method achieves better performance than state-of-the-art methods in sentiment analysis.
Toward Optimal Prediction Error Expansion-Based Reversible Image Watermarking

Reversible image watermarking is a technique that allows the cover image to remain unmodified after watermark extraction. Prediction error expansion-based schemes are currently the most efficient and widely used class of reversible image watermarking techniques. In this paper, first, we prove that the bounded capacity distortion minimization problem for prediction error expansion-based reversible watermarking schemes is NP-hard, and the corresponding decision version of the problem is NP-complete. Then, we prove that the dual problem of bounded distortion capacity maximization problem for prediction error expansion-based reversible watermarking schemes is NP-hard, and the corresponding decision problem is NP-complete. Furthermore, taking advantage of the integer linear programming formulations of the optimization problems, we find the optimal performance metric values for a given image, using concepts from the optimal linear prediction theory. Our technique allows the calculation of these performance metric limit without assuming any particular prediction scheme. The experimental results for several common benchmark images are consistent with the calculated performance limits validate our approach.
Recommender Systems for Online Video Game Platforms: the Case of STEAM

The world of video games has changed considerably over the recent years. Its diversification has dramatically increased the number of users engaged in online communities of this entertainment area, and consequently, the number and types of games available. This context of information overload underpins the development of recommender systems that could leverage the information that the video game platforms collect, hence following the trend of new games coming out every year. In this work we test the potential of state-of-the-art recommender models based respectively on Factorization Machines (FM), deep neural networks (DeepNN) and one derived from the mixture of both (DeepFM), chosen for their potential of receiving multiple inputs as well as different types of input variables. We evaluate our results measuring the ranking accuracy of the recommendation and the diversity/novelty of a recommendation list. All the algorithms achieve better results than a baseline based on implicit feedback (Alternating Least Squares model). The best performing algorithm is DeepNN, the high order interactions are more important than the low order ones for this recommendation task. We also analyze the effect of the sentiment extracted directly from game reviews, and find that it is not as relevant for recommendation as one might expect. We are the first in studying the aforementioned recommender systems over the context of online video game platforms, reporting novel results which could be used as baseline in future works.
Breast cancer diagnosis through active learning in content-based image retrieval

One of the cornerstones of content-based image retrieval (CBIR) for medical image diagnosis is to select the images that present higher similarity with a given query image. Different from previous literature efforts, the present work aims to seamlessly fuse a powerful machine learning strategy based on the active learning paradigm, in order to obtain greater efficacy regarding similarity queries in medical CBIR systems. To do so, we propose a new approach, named as Medical Active leaRning and Retrieval (MARRow) to aid the breast cancer diagnosis. It enables to deal with more feasible strategies, specifically for the medical context and its inherent constraints. We also proposed an active learning strategy to select a small set of more informative images, considering selection criteria based on not only similarity, but also on certain degrees of diversity and uncertainty. To validate our proposed approach, we performed experiments using public medical image datasets, different descriptors for each one and compared our approach against four widely applied and well-known literature approaches, such as: Traditional CBIR without relevance feedback strategies, Query Point Movement Strategy (QPM), Query Expansion (QEX) and SVM Active Learning (SVM-AL). From the experiments, we can observe that our approach presents a strong performance over state-of-the-art ones reaching a precision gain of up to 87.3%. MARRow also presented a well-suited and consistent increasing rate along the learning iterations. Moreover, our approach can significantly minimize the expert’s involvement in the analysis and annotation process (reducing up to 88%). The results testify that MARRow improves the precision of the similarity queries. It is capable to explore at the maximum the experts’ intentions, which are captured during the relevance feedback process, incrementally improving the learning model. Therefore, our approach can be suitable and applied in challenging processes, such as real and medical contexts, enhancing medical decision support systems (e.g. breast cancer diagnosis).
Airborne hyperspectral data for mineral mapping in Southeastern Rajasthan, India

Geology majorly deals with the structures, features, minerals, rocks, etc. of the planet Earth. Geologists are using the technology of remote sensing for structural interpretation and regional mapping. Ores and minerals identification are also done with the help of remote sensing. This paper is mainly focused on the identification of minerals and mapping with the help of various algorithms such as Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Mixture Tuned Matched Filtering (MTMF) using airborne hyperspectral data. Minerals are identified on the basis of the visible and near-infrared spectral reflectance. Spectral reflectance is having absorption features at different positions and absorption peaks are used for the analysis of imagery. This technique provides surface mineralogical details. SAM algorithm mainly computes the angle between the unknown pixel spectrum and unique pixel spectrum. SFF algorithm matched the continuum removed the spectrum of the imagery pixel from the continuum removed reference spectra. MTMF algorithm detects the abundance of the minerals and removes the erroneous positive. Total 13 endmembers (minerals) were identified in the study area. These minerals are grouped into clay minerals, iron minerals, carbonate minerals, and other minerals. These endmembers are used for the mineral map creation from different algorithms. Algorithms produce the diverse kind of mineral map and these are compared with each other on the basis of the mineralogy and discrimination of mineralized region from settlements. Mineral map produced by MTMF algorithm provides convenient results with better accuracy than other algorithms.