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Abstract Background Incidence of renal dysfunction and risks of progression to end-stage renal disease (ESRD) were reported higher in upper urinary tract urothelial carcinoma (UTUC) than in renal cell carcinoma (RCC) patients after unilateral nephrectomy. Methods Totally 193 renal cancer patients, including 132 UTUC and 61 RCC, were studied to clarify whether the pathological changes of the kidney remnant removed from nephrectomy and the clinical factors might predict the risk of ESRD. Renal tubulointerstitial (TI) score and global glomerulosclerosis (GGS) rate were examined by one pathologist and two nephrologists independently under same histopathological criteria. Results The glomerular filtration rates at the time of surgery were lower in UTUC than RCC groups (p

Summary: Human emotions can be recognized from facial expressions captured in videos. It is a growing research area in which many have attempted to improve video emotion detection in both lab‐controlled and unconstrained environments. While existing methods show a decent recognition accuracy on lab‐controlled datasets, they deliver much lower accuracy in a real‐world uncontrolled environment, where a variety of challenges need to be addressed such as variations in illumination, head pose, and individual appearance. Moreover, automatically identifying the key frames consisting of the expression from real‐world videos is another challenge. In this article, to overcome these challenges, we provide a video emotion recognition via multiple feature fusion method. First, a uniform local binary pattern (LBP) and the scale‐invariant feature transform features are extracted from each frame in the video sequences. By applying a random forest classifier, all of the static frames are then labelled by the related emotion class. In this way, the key frames can be automatically identified, including neutral and other expressions. Furthermore, from the key frames, a new geometric feature vector and the LBP from three orthogonal planes are extracted. To further improve robustness, audio features are extracted from the video sequences as an additional dimension to augmenting visual facial expression analysis. The audio and visual features are fused through a kernel multimodal sparse representation. Finally, the corresponding emotion labels to the video sequences can be assigned when a multimodal quality measure specifies the quality of each modality and its role in the decision. The results on both acted facial expressions in the Wild and MMI datasets demonstrate that the proposed method outperforms several counterpart video emotion recognition methods. [ABSTRACT FROM AUTHOR]

Then we have HT ht . Continue this process until all of HT ht and HT ht are removed. Then we have HT ht . If all of the I p i SB I i i sb is removed, the left side of the equality is 1, so there is no left HT ht . [Extracted from the article]

Discovering traffic anomaly propagation enables a thorough understanding of traffic anomalies and dynamics. Existing methods, such as Outlier-Tree, are not accurate to find out the trend of abnormal traffic for two reasons. First, they discover the propagation pattern based on the detected traffic anomalies. The imperfection of the detection method itself may introduce false anomalies and miss the real anomaly. Second, they develop a propagation tree of anomalies by searching continuous spatial and temporal outlier neighborhoods rather than considering from a global perspective, and thus cannot form a complete propagation tree if a spatial or temporal gap exists. In this paper, we propose a novel discovering traffic anomaly propagation method using the mesh data and enhanced traffic change peaks (en-TCP) to visualize the change of traffic anomalies (e.g., an area where vehicles are gathering or evacuating) and thus accurately capture traffic anomaly propagation. Inspired by image processing techniques, the GPS trajectory dataset in each time bin can be converted to one grid traffic image and be stored in the grid density matrix, in which the grid cell corresponds to the pixel and the density of grid cells corresponds to the Gray level (0 ∼ 2 5 5) of pixels. An enhanced adaptive filter is developed to generate traffic change graph sequences from grid traffic images in consecutive periods, and clustering en-TCP in a continuous period is to discover the propagation of traffic anomalies. The accuracy and effectiveness of the proposed method have been demonstrated using a real-world GPS trajectory dataset. [ABSTRACT FROM AUTHOR]

Eigenvalue decomposition is widely used in dimensionality reduction for knowledge engineering, in particular principal component analysis and other similar spectral methods. Traditional eigenvalue decomposition algorithms for decomposing a matrix of size n × n are usually of complexity O(n3), due to a bottleneck in using Householder/Givens transforms to convert a general matrix to a tri-diagonal one. It is proposed in this article a new algorithm that takes only O(n2.584963 log 1/ɛ) computational complexity to achieve accuracy ɛ of eigenvalue decomposition for any ɛ > 0 . The basic idea of our algorithm is to convert a matrix into a diagonal form in multi-scale divide and conquer scheme, and the conversion is to iteratively and recursively apply two phases of operations called diagonal attractions and diagonal absorptions respectively. In a diagonal attraction, it attracts the off-diagonal entries to make the entries nearer to the diagonal larger in magnitude than those farther away from the diagonal. In a diagonal absorption, it absorbs the near-to-diagonal nonzero entries into the diagonal. In such a scheme, no Householder or Givens transforms are involved. Moreover, diagonal attractions and diagonal absorptions can be implemented with fast matrix multiplications. The scheme’s divide and conquer pattern also allows our algorithm to be easily mapped to modern computer hardware. Our algorithm also complements well the family of randomized eigenvalue/SVD algorithms using sampling techinques, which are of complexity O(nα polylog (1/ɛ)) with small α but very large overheads in the polylog. Their strength in the small exponent α of n in complexity was easily cancelled by the exploding overheads in the polylog. Now, with their low-accuracy estimate refined by our algorithm for high accuracy, their strength can be boosted significantly. [ABSTRACT FROM AUTHOR]

In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein–protein interaction identification, including human–pathogen protein–protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human–bacterium protein–protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host–pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs. [ABSTRACT FROM AUTHOR]

Summary: Presented by the avalanche of biological interactions data, computational biology is now facing greater challenges on big data analysis and solicits more studies to mine and integrate cloud‐based multiomics data, especially when the data are related to infectious diseases. Meanwhile, machine learning techniques have recently succeeded in different computational biology tasks. In this article, we have calibrated the focus for host‐pathogen protein‐protein interactions study, aiming to apply the machine learning techniques for learning the interactions data and making predictions. A comprehensive and practical workflow to harness different cloud‐based multiomics data is discussed. In particular, a novel two‐layer machine learning model, namely APEX2S, is proposed for discovery of the protein‐protein interactions data. The results show that our model can better learn and predict from the accumulated host‐pathogen protein‐protein interactions. [ABSTRACT FROM AUTHOR]

Shapelets are subsequences of time-series that represent local patterns and can improve the accuracy and the interpretability of time-series classification. The major task of time-series classification using shapelets is to discover high quality shapelets. However, this is challenging since local patterns may have various scales/lengths rather than a unified scale. In this paper, we resolve this problem by discovering shapelets with multiple scales. We propose a novel Multi-Scale Shapelet Discovery (MSSD) algorithm to discover expressive multi-scale shapelets by extending initial single-scale shapelets (i.e., shapelets with a unified scale). MSSD adopts a bi-directional extension process and is robust to extend single-shapelets obtained by different methods. A supervised shapelet quality measurement is further developed to qualify the extension of shapelets. Comprehensive experiments conducted on 25 UCR time-series datasets show that multi-scale shapelets discovered by MSSD improve classification accuracy by around 10% (in average), compared with single-scale shapelets discovered by counterpart methods. [ABSTRACT FROM AUTHOR]

Byline: Lin Liu, Qiang Liu, Chi-Hung Chi, Zhi Jin, Eric Yu In this paper, we propose a formalism for service requirements and capability modelling. It adopts concepts from the agent-oriented requirements modelling framework i*, which can be used as a means of studying the requirements and architecture for distributed agent systems. We argue that a social modelling framework such as i*, extended with the necessary service-related concepts and formal reasoning mechanisms, offers a better understanding of the social/organisational relationship in an open services world. By explicitly representing the underlying assumptions and the essential factors of services, a semiformal requirements model in i* can automatically evolve and be refined into a service requirements and capability reasoning framework. Eventually, it will assist intelligent agents with certain knowledge and intentions to make intelligent, rational decisions during service discovery, publication, selection and binding within an open services community.

There is a growing trend recently in big data analysis that focuses on behavior interiors, which concern the semantic meanings (e.g., sentiment, controversy, and other state-dependent factors) in explaining the human behaviors from psychology, sociology, cognitive science, and so on, rather than the data per se as in the case of exterior dimensions. It is more intuitive and much easier to understand human behaviors with less redundancy in concept by exploring the behavior interior dimensions, compared with directly using behavior exteriors. However, they usually approach from a unidimensional perspective with a lack of a sense of interrelatedness. Thus, integrating multiple behavior dimensions together into some numerical measures to form a more comprehensive view for subsequent prediction processes becomes a pivotal issue. Moreover, these studies usually focus on the magnitude but neglect the associated temporal features. In this paper, we propose a behavior interior dimension-based neighborhood collaborative filtering method for the top-N hashtag adoption frequency prediction that takes into account the interdependence in temporal dynamics. Our proposed approach couples the similarity in user preference and their impact propagation, by integrating the linear threshold model and the enhanced CF model based on behavior interiors. Experiments on Twitter demonstrate that the behavior-interior-aware CF models achieve better adoption prediction results than the state-of-the-art methods, and the joint consideration of similarity in user preference and their impact propagation results in a significant improvement than treating them separately. [ABSTRACT FROM AUTHOR]

Due to the popularity of web services on the Internet, it is important to have a clear view of their utilization behaviors. Despite asynchronous service invocations and distributed executions can provide better user experience, our views are blurred by out-of-order and fragmented service logs. Researchers have been trying various methods to reveal emerging service orchestration patterns, but nearly all of them have taken deterministic approaches. Hence, they do not natively cater for incomplete data and noises. In this paper, we propose to address these problems by using topic models aiming to reveal service orchestration patterns from sparse service logs. Probabilistic approaches do not only tolerate data defects, but their associated approximation methods also overcome combinatorial explosion. We first investigate the implications of sparsity on topic models. Secondly, we propose an extended time-series form of susceptible-infectious-recovered model to monitor the dynamics of emerging service orchestrations. We quantify their emerging-potential by estimated effective-reproduction-number, which is obtained incrementally by Bayesian parameter estimations. Guided by our proposed emerging-potential measure, one can profile and categorize emerging service orchestration patterns, and generate automated alerts on upcoming consumption peaks. In practice, our model enables service providers to better allocate their resources to meet demands dynamically. While our findings affirm that biterm topic model can be applied to service logs with short and sparse log entries, the effectiveness of our proposed monitoring solutions is also shown by experiments. [ABSTRACT FROM PUBLISHER]

Service recommendation systems have been trying to utilize context-aware information to recommend services that better meet the needs of the service consumers. However, current context-aware service recommendation techniques are mainly based on individual intelligence or the local knowledge of users, and do not take into consideration the common knowledge among different users. To address this, recent research has attempted to use role-based approaches to recommend services to other members within the same context group. However, these proposed algorithms are inefficient and may not scale to cope with the large amount of mobile traffic in the real-world. This paper proposes novel algorithms with better runtime complexity, and further extends them to a MapReduce style to take advantage of popular distributed computing platforms. Experiments running on a medium-sized high performance computing cluster demonstrate that our proposed algorithms outperform previous work in runtime complexity and scalability. [ABSTRACT FROM PUBLISHER]

Bias in peer review entails systematic prejudice that prevents accurate and objective assessment of scientific studies. The disparity between referees' opinions on the same paper typically makes it difficult to judge the paper's quality. This article presents a comprehensive study of peer review biases with regard to 2 aspects of referees: the static profiles (factual authority and self-reported confidence) and the dynamic behavioral context (the temporal ordering of reviews by a single reviewer), exploiting anonymized, real-world review reports of 2 different international conferences in information systems / computer science. Our work extends conventional bias research by considering multiple biases occurring simultaneously. Our findings show that the referees' static profiles are more dominant in peer review bias when compared to their dynamic behavioral context. Of the static profiles, self-reported confidence improved both conference fitness and impact-based bias reductions, while factual authority could only contribute to conference fitness-based bias reduction. Our results also clearly show that the reliability of referees' judgments varies along their static profiles and is contingent on the temporal interval between 2 consecutive reviews. [ABSTRACT FROM AUTHOR]

In order to choose from a list of functionally similar services, users often need to make their decisions based on multiple QoS criteria they require on the target service. In this process, different users may follow different decision making strategies, some are compensatory in which only an overall value on all the criteria is evaluated, some evaluate one criterion at a time in the order of their importance levels, while others count on the number of winning criteria. Most of the current QoS-based service selection systems do not consider these decision strategies in the ranking process, which we believe are crucial for generating accurate ranking results for individual users. In this paper, we propose a decision strategy based service ranking model. Furthermore, considering that different users follow different strategies in different contexts at different times, we apply a machine learning algorithm to learn a personalized ranking model for individual users based on how they select services in the past. We have implemented and tested the proposed approach, and our experiment results show the effectiveness of the approach. [ABSTRACT FROM PUBLISHER]

Group behavior interactions, such as multirobot teamwork and group communications in social networks, are widely seen in both natural, social, and artificial behavior-related applications. Behavior interactions in a group are often associated with varying coupling relationships, for instance, conjunction or disjunction. Such coupling relationships challenge existing behavior representation methods, because they involve multiple behaviors from different actors, constraints on the interactions, and behavior evolution. In addition, the quality of behavior interactions are not checked through verification techniques. In this paper, we propose an ontology-based behavior modeling and checking system (OntoB for short) to explicitly represent and verify complex behavior relationships, aggregations, and constraints. The OntoB system provides both a visual behavior model and an abstract behavior tuple to capture behavioral elements, as well as building blocks. It formalizes various intra-coupled interactions (behaviors conducted by the same actor) via transition systems (TSs), and inter-coupled behavior aggregations (behaviors conducted by different actors) from temporal, inferential, and party-based perspectives. OntoB converts a behavior-oriented application into a TS and temporal logic formulas for further verification and refinement. We demonstrate and evaluate the effectiveness of the OntoB in modeling multirobot behaviors and their interactions in the Robocup soccer competition game. We show, that the OntoB system can effectively model complex behavior interactions, verify and refine the modeling of complex group behavior interactions in a sound manner. [ABSTRACT FROM AUTHOR]