Reviewerly/exHarmony huggingface dataset:
SUSiNet: See, Understand and Summarize It

In this work we propose a multi-task spatio-temporal network, called SUSiNet, that can jointly tackle the spatio-temporal problems of saliency estimation, action recognition and video summarization. Our approach employs a single network that is jointly end-to-end trained for all tasks with multiple and diverse datasets related to the exploring tasks. The proposed network uses a unified architecture that includes global and task specific layer and produces multiple output types, i.e., saliency maps or classification labels, by employing the same video input. Moreover, one additional contribution is that the proposed network can be deeply supervised through an attention module that is related to human attention as it is expressed by eye-tracking data. From the extensive evaluation, on seven different datasets, we have observed that the multi-task network performs as well as the state-of-the-art single-task methods (or in some cases better), while it requires less computational budget than having one independent network per each task.
Guided Motion Planning for Snake-like Robots Based on Geometry Mechanics and HJB Equation

In this paper, a novel guided motion planning method is proposed for snake-like robots, which decomposes the configuration space into the fiber space and the gait space, with the motion planned in the low-dimensional fiber space, so as to avoid the dimensionality curse problem. More specifically, first, based on geometry mechanics, the kinematic connection is derived, which successfully maps the motion in the fiber space into that in the gait space. According to the kinematic connection, a novel bidirectional motion control method for snake-like robots is proposed to improve the flexibility that can produce not only the forward locomotion but also the backward locomotion additionally. The convergence of the bidirectional motion control method is proved based on averaging theory. Then, the motion planning problem is solved in the low-dimensional fiber space, for which the HJB equation is used to generate the optimal control in the fiber space to avoid the well-known curse of dimensionality. Finally, combining the bidirectional motion control and the motion planned in the fiber space, a guided motion planning method in the configuration space is proposed. Numerical simulations and experiments based on the real snake-like robot platform are performed, whose results demonstrate that the proposed algorithm is valid and robust.
A total variation and group sparsity based tensor optimization model for video rain streak removal

Rain streak removal is an important issue of the outdoor vision system and has been investigated extensively. In this paper, we propose a novel tensor optimization model for video rain streak removal by fully considering the discriminatively intrinsic characteristics of the rain streaks and the clean video. In specific, the rain streaks are group sparse and smooth along the rain streaks direction; the clean video is smooth along the perpendicular direction of the rain streaks and the temporal direction. For the rain streaks, we use ℓ2,1 norm to characterize the group sparsity and the unidirectional total variation (UTV) to promote the smoothness along the rain streaks direction. For the clean video, we use two UTV regularizers to enhance the smoothness along the perpendicular direction of the rain streaks and the temporal direction. To solve the proposed model we develop an efficient alternating direction method of multipliers (ADMM) algorithm. Experiments on synthetic and real data demonstrate the superiority of the proposed method over state-of-the-art methods both quantitatively and qualitatively.
A comparative study of different texture features for document image retrieval

Due to the rapid increase of different digitised documents, there has been significant attention dedicated to document image retrieval over the past two decades. Finding discriminative and effective features is a fundamental task for providing a fast and more accurate retrieval system. Texture features are generally fast to compute and are suitable for large volume data. Thus, in this study, the effectiveness of texture features widely used in the literature of content-based image retrieval is investigated on document images. Twenty-six different texture feature extraction methods from four main categories of texture features, statistical, transform, model, and structural-based approaches, are considered in this research work to compare their performance on the problem of document image retrieval. Three document image datasets, MTDB, ITESOFT, and CLEF_IP with various content and page layouts are used to evaluate the twenty-six texture-based features on document image retrieval systems. The retrieval results are computed in terms of precision, recall and F-score, and a comparative analysis of the results is also provided. Feature dimensions and time complexity of the texture-based feature methods are further compared. Finally, some conclusions are drawn and suggestions are made about future research directions.
Privacy preservation of data using crow search with adaptive awareness probability

Information security and privacy in the healthcare sector is a concern of increasing significance. The adoption of increased regulation, digital patient records, provider consolidation, and the growing requirement for information among patients, payers, and providers, all point towards the necessitation for improved information security. In most of the traditional anonymization methods, it is more robust on data sanitization process, yet not on data restoration. Even though few privacy preservation algorithms have been introduced recently, the accuracy of sanitization seems to be low. Hence, this paper intends to develop enhanced medical data preservation in healthcare data. The proposed scheme mainly concerns on setting up an effective sanitizing approach for hiding the sensitive rules offered by clients. For sanitizing the medical data, a secure key is generated which optimally selected using improved Crow Search Algorithm (CSA) known as Adaptive Awareness Probability-based CSA (AAP-CSA). In addition, the sanitized medical data is restored securely by the authorized user. Moreover, the proposed scheme is compared with other conventional algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and Crow Search Optimization (CSA) and the results are obtained.
Fast transfer Gaussian process regression with large-scale sources

In transfer learning, we aim to improve the predictive modeling of a target output by using the knowledge from some related source outputs. In real-world applications, the data from the target domain is often precious and hard to obtain, while the data from source domains is plentiful. Thus, since the complexity of Gaussian process based multi-task/transfer learning approaches grows cubically with the total number of source＋ target observations, the method becomes increasingly impractical for large (>104) source data inputs even with a small amount of target data. In order to scale known transfer Gaussian processes to large-scale source datasets, we propose an efficient aggregation model in this paper, which combines the predictions from distributed (small-scale) local experts in a principled manner. The proposed model inherits the advantages of single-task aggregation schemes, including efficient computation, analytically tractable inference, and straightforward parallelization during training and prediction. Further, a salient feature of the proposed method is the enhanced expressiveness in transfer learning — as a byproduct of flexible inter-task relationship modelings across different experts. When deploying such models in real-world applications, each local expert corresponds to a lightweight predictor that can be embedded in edge devices, thus catering to cases of online on-mote processing in fog computing settings.
Data locality optimization based on data migration and hotspots prediction in geo-distributed cloud environment

With the explosive growth of data-intensive mobile, social, commercial and industrial applications, geo-distributed cloud becomes the main trend of cloud computing due to its advantages of higher flexible scalability, stronger stability, lower latency, and more diverse services. Due to the limited network bandwidth, communication across geographic data centers typically suffers from wide-area latencies, which significantly deteriorates system performance. Data locality is an effective way to solve this problem. In order to provide flexible cloud computing services for data-intensive applications, combining with the advantage of geo-distributed cloud computing paradigm, this paper proposed a data locality optimization method based on data migration (DLO-Migrate) and a data locality optimization algorithm based on hotspots prediction (DLO-Predict) to reduce data access delay in geo-distributed cloud environment. In DLO-Migrate method, tasks are assigned according to node locality, and access data of non-node-locality tasks are migrated in advance by using the idle network bandwidth. In DLO-Predict algorithm, from cloud-level data locality perspective, hot files are predicted and synchronized periodically among data centers of the geo-distributed cloud during information interaction. Extensive experimental results show that, compared with baseline algorithms, our proposed algorithms can improve data locality of geo-distributed cloud and reduce job completion time substantially.
Learning peer recommendation using attention-driven CNN with interaction tripartite graph

Learning peer recommendation (LPR) is one of the effective solutions to overcome the information load of learners. This paper presents a multi-objective LPR framework for online learning. Using a dynamic interaction tripartite graph (DITG), we characterize and model the complex relationships among learners, learning content, and interaction behaviours, followed by capturing the dynamic interactions among learners with an attention-driven convolution neural network (CNN). The proposed attention-driven CNN is leveraged to tune the weights of interaction behaviours according to the features of the learning content. A multi-objective function composed of three conflicting metrics, interaction intensity, diversity and novelty, is optimized to achieve simultaneous multiple recommendations for a group of learners. Compared to the state-of-the-art approaches, the proposed LPR framework and algorithms perform favourably.
QoS Promotion in Energy-Efficient Datacenters Through Peak Load Scheduling

To build energy-efficient datacenters, one widely used way is to dynamically manipulate the quantity of available hardware resources on demand. However, when bursty workloads appear, additional time overhead is required for resource gearing, thus incurring a performance degradation. To explore this problem, we present an intelligible analysis on the request handing in a VM. From the analysis, we find that, when workloads are overloaded (i.e., peak loads appear), the number of QoS guaranteed requests can be greatly increased by deferring the scheduling of a few requests. Inspired by this finding, we propose a Peak Load Scheduling Control (PLSC) method to promote the Quality of Service (QoS) of peak loads for modern energy-efficient datacenters. However, peak loads are usually difficult to identify. To overcome this difficulty, PLSC tracks the number of requests residing in a VM by leveraging a two-tier request queue maintained by it. When the number exceeds the capability of the VM, it means that peak loads appear. In this case, PLSC adds some delay-tolerant requests to the secondary queue. The scheduling of requests in the secondary queue is controlled with a lower priority than that of requests in the primary queue. Sequentially, with critical requests maintained in the primary queue, PLSC shortens the response time of critical requests. In addition, PLSC expands the number of QoS-guaranteed requests. Comprehensive experiments are conducted to attest the effectiveness of PLSC, by simulating a typical energy-efficient datacenter. The experimental results show that PLSC significantly promotes the QoS of workloads with a negligible impact on energy saving.
Securing content-centric networks with content-based encryption

As content is transmitted in content-driven manner in the content-centric network (CCN), it does not require any host address; therefore, it is infeasible to establish a traditional secure channel between hosts. Securing the content transmission in the CCN is a challenging problem. We solve this problem with the content-based encryption, where the encryption key is associated with the content itself, and the private decryption keys are distributed to the authorized consumers. To deal with the security requirements for content-based encryption, we define a security model that captures the key existential unforgeability and semantic security. We then propose a content-based encryption scheme with short ciphertexts, which is proven to be strong key existentially unforgeable and semantically secure in the standard model. We apply the scheme to construct a secure content transmission protocol in the CCN, which captures the security properties of content confidentiality, integrity, resistance to replay attacks and resistance to key forgery attacks. The performance analysis shows that our protocol is efficient for large content transmission.
A Novel Hot Topic Detection Framework With Integration of Image and Short Text Information From Twitter

Twitter exhibits several characteristics, including a limited number of features and noisy text information.Extracting valuable information from Twitter has made hot topic detection a challenging task.In this paper, a novel four-stage framework is proposed to improve the performance of topic detection.Data preprocessing is the first stage.Deep learning is then exploited to enrich short text information via image understanding.Next, improved latent Dirichlet allocation is used to optimize the image effective word pairs, which improves the accuracy of the extracted topic words.Finally, both short text and images are integrated for topic detection, in which the corresponding topics are mined based on fuzzy matching of topic words.A large number of experiments show that the proposed framework significantly improves the performance of topic detection and outperforms the selected baseline methods.
A transform-based fast fuzzy C-means approach for high brain MRI segmentation accuracy

Segmentation of brain magnetic resonance (MR) images has a significant impact on the computer-aided diagnosis and analysis. However, due to the presence of noise in medical images, many segmentation methods suffer from limited segmentation accuracy. To reduce the effect of noise and achieve high segmentation accuracy many approaches based on the local and nonlocal information in the spatial domain have been proposed in the past. Recently, the authors have proposed a discrete cosine transform (DCT)-based local and nonlocal fuzzy C-means method (DCT-LNLFCM) which performs much better than the existing methods. However, the method is slow in speed. This paper presents a fast DCT-based nonlocal fuzzy C-means (DCT-NLFCM) segmentation method which is not only very fast than the DCT-LNLFCM, but also provides better segmentation results. The proposed method uses DCT-based MR pre-filtered image to achieve high segmentation accuracy and its histogram enables to achieve very high computation speed. Detailed experiments are conducted to establish the superiority of the proposed method over the state-of-the-art unsupervised methods.
A detail preserving variational model for image Retinex

• A new variational model for image retinex is proposed. • The existence of minimizer for the variational model is proved. • An efficient algorithm is derived to solve the model by adding auxiliary variables. • The convergence of the numerical algorithm is proved under some assumptions. • The proposed method can be extended to other regularization and fidelity terms. In this paper, we propose a detail preserving variational model for Retinex to simultaneously estimate the illumination and the reflectance from an observed image. Most previous models use the log-transform as pretreatment which results in loss of details in reflectance. From this observation, a detail preserving variational method is proposed for better decomposition. Different from the log-transform based models, the proposed model performs the decomposition directly in the image domain. Mathematically, we prove the existence of a solution for the proposed model. Numerically, we derive an efficient iterative algorithm by utilizing alternating direction method of multipliers (ADMM) method. Experimental results demonstrate the effectiveness of the proposed method. Compared with other closely related Retinex methods, the proposed method achieves competitive results on both subjective and objective assessments.
Abnormal Group-Based Joint Medical Fraud Detection

Joint fraud is one of the most common fraud types existing in medical fraud.However, joint fraud detection is a difficult problem because fraudsters take only a very small part of the population and fraudsters do everything to bypass fraud detection constraints.Most existing fraud detection studies focus on finding normal behavior patterns and treat those who possess behaviors that violate behavior patterns as fraudsters.However, these methods generally have high false positives because normal people may also sometime behave contrary to normal behavior patterns.To address this issue, we propose an abnormal group-based joint fraud detection method named abnormal group-based joint fraud detection method.This method can distinguish suspicious fraudsters from normal persons who have unusual behaviors by abnormal group mining in person similarity adjacency graph so that the occurrence of false positives caused by nonfraudulent abnormal behavior can be reduced.The extensive experiments using medical insurance data show that our approach has improved the precision of joint fraud detection by more than 10% compared with the conventional methods.
A Concept Map-Based Learning Paths Automatic Generation Algorithm for Adaptive Learning Systems

A concept map is an important knowledge visualization tool in adaptive learning systems.The weak concepts of students can be identified by analyzing the concept maps to provide learning guidance and teaching suggestions to students and teachers.However, in the current researches, it is difficult to analyze effective information from the concept maps when the number of concepts is large and the associations between concepts are complicated.It rarely reflects the learning performance of different student groups, which do not reflect the characteristics of the adaptive learning systems.A learning path automatic generation algorithm, Learning paths generation (LPG) algorithm, for adaptive learning systems is proposed.The LPG algorithm fully considers the learning performance of different student groups.The concept maps with different learning features are generated by the clustering algorithm and association rules mining, and several simplified learning paths are generated by using the topological sorting algorithm.Experiments show that the LPG algorithm has a good discrimination for the student groups and can generate different learning paths according to the students' learning performance.
Are social media companies motivated to be good corporate citizens? Examination of the connection between corporate social responsibility and social media safety

This paper explores the connection between corporate social responsibility and social media safety. By examining the legal framework governing social platforms in the United States and case studies of online harms, we explore whether current U.S. laws and company content moderation policies are effective in eliminating content (revenge porn and acts of terrorism) that is universally agreed to be harmful. Finally, the paper makes a number of suggestions for improvements in policy.
BRB: Mitigating Branch Predictor Side-Channels

Modern processors use branch prediction as an optimization to improve processor performance. Predictors have become larger and increasingly more sophisticated in order to achieve higher accuracies which are needed in high performance cores. However, branch prediction can also be a source of side channel exploits, as one context can deliberately change the branch predictor state and alter the instruction flow of another context. Current mitigation techniques either sacrifice performance for security, or fail to guarantee isolation when retaining the accuracy. Achieving both has proven to be challenging. In this work we address this by, (1) introducing the notions of steady-state and transient branch predictor accuracy, and (2) showing that current predictors increase their misprediction rate by as much as 90% on average when forced to flush branch prediction state to remain secure. To solve this, (3) we introduce the branch retention buffer, a novel mechanism that partitions only the most useful branch predictor components to isolate separate contexts. Our mechanism makes thread isolation practical, as it stops the predictor from executing cold with little if any added area and no warm-up overheads. At the same time our results show that, compared to the state-of-the-art, average misprediction rates are reduced by 15-20% without increasing area, leading to a 2% performance increase.
Recurrent Neural Network Language Model Adaptation for Multi-Genre Broadcast Speech Recognition and Alignment

Recurrent neural network language models (RNNLMs) generally outperform n-gram language models when used in automatic speech recognition (ASR). Adapting RNNLMs to new domains is an open problem and current approaches can be categorised as either feature-based or model based. In feature-based adaptation, the input to the RNNLM is augmented with auxiliary features whilst model-based adaptation includes model fine-tuning and the introduction of adaptation layer(s) in the network. In this paper, the properties of both types of adaptation are investigated on multi-genre broadcast speech recognition. Existing techniques for both types of adaptation are reviewed and the proposed techniques for model-based adaptation, namely the linear hidden network adaptation layer and the K-component adaptive the RNNLM, are investigated. Moreover, new features derived from the acoustic domain are investigated for the RNNLM adaptation. The contributions of this paper include two hybrid adaptation techniques: the fine-tuning of feature-based RNNLMs and a feature-based adaptation layer. Moreover, the semi-supervised adaptation of RNNLMs using genre information is also proposed. The ASR systems were trained using 700 h of multi-genre broadcast speech. The gains obtained when using the RNNLM adaptation techniques proposed in this paper are consistent when using RNNLMs trained on an in-domain set of 10M words and on a combination of in-domain and out-of-domain sets of 660 M words, with approx. 10% perplexity and 2% relative word error rate improvements on a 28.3 h. test set. The best RNNLM adaptation techniques for ASR are also evaluated on a lightly supervised alignment of subtitles task for the same data, where the use of RNNLM adaptation leads to an absolute increase in the F-measure of 0.5%.