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关键词： Testing   Color   Software   Complexity theory   Combinatorial testing   Licenses   Computer science   Pending schema   minimal failure-causing schema   combinatorial testing   software testing  

摘要： Combinatorial Testing (CT) is an effective testing technique for detecting failures which are triggered by the interactions of various factors that influence the behaviour of a system. Although many studies in CT have designed elaborate test suites (called covering arrays) to systemically check each possible factor interaction, they provide weak support to locate the concrete failure-inducing interactions, i.e., the Minimal Failure-causing Schemas (MFS). To this end, a variety of MFS identification approaches have been proposed. However, as this study reveals, these approaches suffer from various issues such as cannot identify multiple overlapping MFSs, cannot handle MFSs with high degrees, cannot be applied to systems with large number of parameters, etc. These issues are essentially caused by the exponential computing complexity of checking every interaction in the test cases. Therefore, they can only focus on a subset of all the possible interactions, resulting in many interactions unnoticed. Ignoring these unnoticed interactions could potentially cause failures that have never been systematically checked. Hence, it is beneficial for MFS identification approaches to identify these interactions. In order to account for these unnoticed interactions in CT, this study introduces the notion of pending schema, based on which a theoretical framework of CT schemas is established. In particular, we formally define the determinability of a schema in CT with respect to given information;as such, the yet-to-be determined schemas are exactly the pending schemas. The relationships between the different schemas (faulty, healthy, and pending) and test cases are also theoretically analyzed. Based on which, we further propose three formulas, along with three corresponding algorithms, for the identification of the pending schemas in failing test cases, and formally prove their correctness. As a result, we reduce the complexity of obtaining pending schemas with respect to the number

关键词： Hash functions   Optimization   Codes   Semantics   Estimation   Computer science   Annotations   Cross-modal retrieval   unsupervised deep hashing   similarity estimation   optimization strategy  

摘要： Cross-modal hashing that leverages hash functions to project high-dimensional data from different modalities into the compact common hamming space, has shown immeasurable potential in cross-modal retrieval. To ease labor costs, unsupervised cross-modal hashing methods are proposed. However, existing unsupervised methods still suffer from two factors in the optimization of hash functions: 1) similarity guidance, they barely give a clear definition of whether is similar or not between data points, leading to the residual of the redundant information;2) optimization strategy, they ignore the fact that the similarity learning abilities of different hash functions are different, which makes the hash function of one modality weaker than the hash function of the other modality. To alleviate such limitations, this paper proposes an unsupervised cross-modal hashing method to train hash functions with discriminative similarity guidance and adaptively-enhanced optimization strategy, termed Deep Adaptively-Enhanced Hashing (DAEH). Specifically, to estimate the similarity relations with discriminability, Information Mixed Similarity Estimation (IMSE) is designed by integrating information from distance distributions and the similarity ratio. Moreover, Adaptive Teacher Guided Enhancement (ATGE) optimization strategy is also designed, which employs information theory to discover the weaker hash function and utilizes an extra teacher network to enhance it. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed DAEH against the state-of-the-arts.

关键词： Euclidean distance   Training   Task analysis   Feature extraction   Testing   Computer science   Object detection   Center loss   deep learning   Euclidean distance center   Pearson correlation coefficient   vehicle re-identification  

摘要： Center loss is widely used as a supervision tool in deep learning method. However, the center loss also has some shortcomings, the most important of which is that it must be combined with softmax loss to run well. In this article, we sum up five shortcomings of center loss and solve all of them by proposing a dual distance center loss (DDCL). Compared with center loss, DDCL can run without the combination of softmax to supervise training the model. In addition, we verify the inconsistency between the proposed DDCL and softmax loss in the feature space. To be specifically, we add the Pearson distance on the basis of the Euclidean distance to the same center, which makes all features of the same class be confined to the intersection of a hypersphere and a hypercone in the feature space, strengthens the intraclass compactness of the center loss, and enhances the generalization ability of center loss. Moreover, by designing a Euclidean distance threshold between all center pairs, we not only strengthen the interclass separability of center loss, but also make the center loss (or DDCL) works well without the combination of softmax loss. We verify the effectiveness of DDCL in four datasets, two of which are widely used in the field of vehicle re-identification named VeRi-776 dataset and VehicleID dataset, and two other datasets are widely used in the field of person re-identification named Market1501 dataset and MSMT17 dataset. The experimental results of the proposed DDCL exceed that of the softmax loss in all the four datasets, indicating that our proposed method not only can run without the combination of softmax, but also has a high accuracy.

关键词： Planning   Search problems   Reinforcement learning   Urban areas   Space exploration   Probabilistic logic   Computer science   Automatic itinerary planning   deep reinforcement learning   multiobjective optimization  

摘要： Automatic itinerary planning that provides an epic journey for each traveler is a fundamental yet inefficient task. Most existing planning methods apply heuristic guidelines for certain objective, and thereby favor popular preferred point of interests (POIs) with high probability, which ignore the intrinsic correlation between the POIs exploration, traveler's preferences, and distinctive attractions. To tackle the itinerary planning problem, this paper explores the connections of these three objectives in probabilistic manner based on a Bayesian model and proposes a triple-agent deep reinforcement learning approach, which generates 4-way direction, 4-way distance, and 3-way selection strategy for iteratively determining next POI to visit in the itinerary. Experiments on five real-world cities demonstrate that our triple-agent deep reinforcement learning approach can provide better planning results in comparison with state-of-the-art multiobjective optimization methods.

关键词： Task-centered instructional strategy   Computer science education   Cognitive load   theory   Social cognitive theory   Instructional design   Whole-task instruction  

摘要： Novice programmers, who have yet to form effective mental models of the domain, often experience high cognitive load, low confidence, and high anxiety, negatively affecting learning and retention rates. These cognitive and affective limitations pose an instructional challenge. This study aimed to investigate the effectiveness of a whole-task instructional approach compared with a part-task instructional approach for novices learning to program from a cognitive and affective perspective. A fully randomized between-subjects controlled experiment was designed, including two online instructional conditions (whole-task vs. part-task). The whole-task condition followed the Task-Centered Instructional Strategy and included explicit instruction in the context of whole tasks. The part-task condition followed a part-task instructional strategy and included the same explicit instruction, yet in the context of objectives and topic-related tasks. Based on Bandura's triadic model (Bandura, Social foundations of thought and action: A social cognitive theory, Prentice-Hall, 1986), we propose a conceptual model, which we used to hypothesize that the Task-Centered Instructional Strategy may be more effective for novices learning to program. Sixty-five students with no programming experience volunteered to participate in the study and were randomly assigned to one of the conditions. Participants in the whole-task condition performed significantly better on the near and far transfer posttests. In accordance with our model, confidence and cognitive load during learning were found to be significant partial mediators of the effect of instructional strategy on performance. Overall, we found that the task-centered instructional strategy, combining explicit instruction with whole-tasks, is effective for addressing the cognitive and affective considerations relevant to novices in computer science education.

关键词： Computer vision system   Meat   Meat products   Carcass  

摘要： Computer vision systems (CVS) are applied to macro- and microscopic digital photographs captured using digital cameras, ultrasound scanners, computer tomography, and wide-angle imaging cameras. Diverse image acquisition devices make it technically feasible to obtain information about both the external features and internal structures of targeted objects. Attributes measured in CVS can be used to evaluate meat quality. CVS are also used in research related to assessing the composition of animal carcasses, which might help determine the impact of cross-breeding or rearing systems on the quality of meat. The results obtained by the CVS technique also contribute to assessing the impact of technological treatments on the quality of raw and cooked meat. CVS have many positive attributes including objectivity, non-invasiveness, speed, and low cost of analysis and systems are under constant development an improvement. The present review covers computer vision system techniques, stages of measurements, and possibilities for using these to assess carcass and meat quality.

关键词： Codes   Encoding   Correlation   Diseases   Semantics   MIMICs   Computer science   Automatic ICD coding   attention network   long-tailed label distribution   label correlation  

摘要： The International Classification of Diseases (ICD) code is a disease classification method formulated by the World Health Organization(WHO). ICD coding usually requires clinicians to manually allocate ICD codes to clinical documents, which is labor-intensive, expensive, and error-prone. Therefore, many methods have been introduced for automatic ICD coding. However, most of the methods have ignored or cannot combine two essential features well: long-tailed label distribution and label correlation. In this paper, we propose a novel end-to-end Joint Attention Network (JAN) to solve these two problems. JAN includes Document-based attention and Label-based attention to capture semantic information from clinical document text and label description, respectively, which helps solve the classification of dense and sparse data in long-tailed label distribution. Besides, an Adaptive fusion layer and CorNet block are presented to adaptively adjust the weight of these two attentions and exploit label co-occurrence relations, respectively. Experiments on the MIMIC-III and MIMIC-II datasets demonstrate that our proposed JAN outperformed previous state-of-art methods achieving Micro-F1 of 0.553, Micro-AUC of 0.989 and precision at top 8(P@8) of 0.735. Finally, we also provide attention and label correlation visualization to verify the effectiveness of our model and improve the interpretation of our deep learning-based method.

关键词： Vehicular ad hoc networks   Transportation   Information exchange   Routing   Computer science   Urban areas   Deep learning   Decision making   deep learning   traffic management   user assistance   VANET  

摘要： Vehicular Ad-Hoc Networks (VANETs) provide roadside communication for improving the ease of driving user information exchange. It interconnects hierarchical infrastructure units and other vehicles for real-time traffic and vehicle management applications. The growth of vehicle and information density requires concord information exchange for application responses. In this article, Vehicle-Consensus Routing Management Scheme (VCRMS) is proposed for achieving fair roadside assistance for driving users. The proposed scheme exploits the surrounding vehicle information for infrastructure selection and traffic management. The infrastructure and vehicle information are analyzed for their similarity using deep learning for extracting monotonous decisions. The current and previous decisions are used for succeeding in information selection and traffic information retrieval. This prevents unnecessary data from being congesting the traffic and vehicle management application during driver assistance. The performance shows that the proposed scheme achieves 10.7% high application response, 15.9% less information delay, and 10.7% less traffic for different vehicle velocities.

关键词： Menstrual cycle   Histological research   National Socialism   Capital punishment  

摘要： Purpose Hermann Stieve (1886-1952), director of the Berlin Anatomical Institute from 1935, benefited from the rise of execution numbers during the "Third Reich". He used organs and tissues from executed women for his histological research on the reproductive organs and investigated the influence of "nervous agitation" on the cyclical changes of endometrium and ovary. It is still controversial how he was able to acquire intimate data on the executed women and it was therefore suggested that some of his data may have been "invented". Methods Newly emerged dissection protocols and histological drawings from Stieve's research, together with archived court records, enable a more detailed analysis of Stieve's published data. Results We extracted 304 case descriptions from Stieve's publications. Of these, 88 could be linked with 33 identifiable women and related historical records. Nearly all reported causes of death and/or verdicts of executed women were false. Reported clinical data, particularly the day of the menstrual cycle and uterine bleeding shortly before death, are more difficult to verify. We found non-standardised documentation and possible confusions of cases, which may in part be attributable to war effects. Conclusion Stieve actively concealed the fate of the executed women, mostly by inventing imaginary stories. This followed a request by the German and Soviet authorities after 1945 not to publish results from cases of political victims, but only from "dangerous criminals". Scientifically relevant clinical data were not always reported correctly, but are not necessarily fraudulent as different interpretations of this finding can be suggested.