The operational, academic, and policy communities disagree on which threats against the power grid are likely and what damage would ensue. For instance, the feasibility and impact of MadIoT-style attacks is being actively debated. By surveying grid experts (N=18) we find that disagreements are not unique to MadIoT attacks but occur across multiple well-studied grid threats. Based on prior work and our survey, we hypothesize that the disagreements stem from inconsistencies in how grid threats are modeled. We identify five likely causes of modeling inconsistencies: 1) using unrealistic grid topologies, 2) assuming unrealistic capabilities for attackers, 3) exploring too few grid scenarios, 4) using incomplete simulators that omit relevant grid processes, and 5) using simulators that incorrectly model key grid processes. To check these hypotheses, we create a modeling framework and examine how these factors change our understanding of the feasibility and impact of grid threats. We use four diverse grid threats as case studies: MadIoT, False Data Injection Attacks, Substation Circuit Breaker Takeover, and Power Plant Takeover. We find that each of our hypothe-sized causes of modeling inconsistencies has a significant effect on modeling the outcomes of attacks. For example, we find that MadIoT attacks are much less feasible and require significantly more high-wattage IoT devices on realistic topologies than on topologies previously used to model them. In contrast, we find that Substation Circuit Breaker Takeover attacks are much more feasible in emergency scenarios and may require significantly fewer substations for failure than previous modeling suggested. We conclude with actionable recommendations for accurately assessing the impact of threats against the grid.

Although a large number of methods for video text detection and recognition have been proposed over the past years, it is hard to find the best state-of-the-art method because of nonavailability of standard datasets, ground truth, and common evaluation measures. Therefore, in this paper, we propose a semiautomatic system for ground truth generation for video text detection and recognition, which includes English and Chinese text of different orientation. The system has a facility to allow the user to manually correct the ground truth if the automatic method produces incorrect results. We propose eleven attributes at the word level, namely: line index, word index, coordinate values of bounding box, area, content, script type, orientation information, type of text (caption/scene), condition of text (distortion/distortion free), start frame, and end frame to evaluate the performance of the method. We also introduce a new dataset that consists of 466 video frames collected from TRECVID 2005 and 2006 databases. The video frames in our dataset contain both horizontal texts (278 frames: 181 with English texts and 97 with Chinese texts) and nonhorizontal texts (188 frames: 140 English and 48 Chinese). Furthermore, the performance of the proposed system is compared with existing text detection methods by calculating measures manually and automatically to show usefulness of our semiautomatic system. The ground truth and the semiautomatic system will be released to the public.

Computer aided diagnosis (CAD) in medical imaging is of growing interest in recent years. Our proposed CAD system aims to enhance diagnosis and prognosis of traumatic brain injury (TBI) patients with hematomas. Hematoma caused by blood vessel rupture is the major lesion in TBI cases and is usually assessed using head computed tomography (CT). In our CAD system, we segment the hematoma region from each slice of a CT series, extract features from the hematoma segments, and automatically classify the hematoma types using machine learning methods. We propose two sets of shape based features for each segmented hematoma region. The first set contains primitive features describing the overall shape of a hematoma region. The features in the second set are based on the dissimilarities of the shapes of hematoma regions measured by geodesic distances. After feature extraction, we classify the hematoma regions into three types -- epidural hematoma, sub-dural hematoma, and intracerebral hematoma, using random forest. Each tree of the random forest votes one class for each hematoma, and the random forest takes the class label with the majority votes for the hematoma. As hematomas are volumetric in nature, some hematomas are observed across several consecutive slices in the same CT series. For each class, we add the votes from each hematoma slice that comprises the volumetric hematoma in that class, then we take the class with the majority of the summed votes as the class label for that volumetric hematoma. The overall classification accuracies for hematoma region from each CT slice are 80.7%, 81.3%, and 81.1% using primitive features only, geodesic distance features only, or both sets of features, respectively. For volumetric hematoma classification, the overall accuracies are 80.9%, 81.5%, and 81.5% respectively. The results are promising to radiologists and neurosurgeons specialized in this field of research.

Automatic medical image classification is difficult because of the lacking of training data. As manual labeling is too costly, we provide an automatic labeling solution to this problem by making use of the radiology report associated with the medical images. We first segment and reconstruct the 3D regions of interest (ROIs) from the medical images, and extract pathology and anatomy information from the associated report. We use an anatomical atlas to map the ROIs to the anatomy part(s) and match the pathology information of the same anatomy part(s) from the text. In this way, the ROIs are automatically labeled with pathology types which can be served as class labels, and a training data set of a large number of training instances is generated automatically. We extract the volume, color, location, and shape features of the ROIs, and classify the types of ROIs using these features. The overall evaluation result is promising to doctors and medical professionals. Our experiment is conducted using traumatic brain injury CT images; however, our framework of automatically labeling and classifying medical cases can be extended to medical images in other modality or of other anatomical part.

Computed tomography (CT) is used widely in traumatic brain injury diagnosis. One axial brain CT scan consists of multiple slices with different heights along the brain axial direction. Indexing of brain CT slices is to order the slices and align each individual slice onto the corresponding brain axial height, which is an important step in content-based image retrieval and computer-assisted diagnosis. Current existing methods for this indexing task are through the image registration techniques by registering 2D image slices onto a 3D brain atlas. In this paper, instead of using the registration methods, we propose a fast indexing method using anatomical feature classification. In our method, the brain CT scan is divided into 6 height levels along the axial direction so that slices in each level share similar anatomical structure. In this way, the indexing problem becomes a classification problem that one series of scan slices are to be classified into 6 classes. Experimental results show that the proposed method is effective and efficient.

Large number of medical images are produced daily in hospitals and medical institutions, the needs to efficiently process, index, search and retrieve these images are great. In this paper, we propose a pathology based medical image annotation framework using a statistical machine translation approach. After pathology terms and regions of interest (ROIs) are extracted from training text and images respectively, we use machine translation model IBM Model 1 to iteratively learn the alignment between the ROIs and the pathology terms and generate an ROI-to-pathology translation table. In testing phase, we annotate the ROI in the image with the pathology label of the highest probability in the translation table. The overall annotation results and the retrieval performance are promising to doctors and medical professionals.

In recent years, with the rapid proliferation of digital images, the need to search and retrieve the images accurately, efficiently, and conveniently is becoming more acute. Automatic image annotation with image semantic content has attracted increasing attention, as it is the preprocess of annotation based image retrieval which provides users accurate, efficient, and convenient image retrieval with image understanding. Different machine learning approaches have been used to tackle the problem of automatic image annotation; however, most of them focused on exploring the relationship between images and annotation words and neglected the relationship among the annotation words. In this paper, we propose a framework of using language models to represent the word-to-word relation and thus to improve the performance of existing image annotation approaches utilizing probabilistic models. We also propose a specific language model - the semantic similarity language model to estimate the semantic similarity among the annotation words so that annotations that are more semantically coherent will have higher probability to be chosen to annotate the image. To illustrate the general idea of using language model to improve current image annotation systems, we added the language model on top of the two specific image annotation models - the translation model (TM) and the cross media relevance model (CMRM). We tested the improved models on a widely used image annotation corpus - the Corel 5K dataset. Our results show that by adding the semantic similarity language model, the performance of image annotation improves significantly in comparison with the original models. Our proposed language model can also be applied to other image annotation approaches using word probability conditioned on image or word-image joint probability as well.

In intracranial pathological examinations using CT scan, brain midline shift (MLS) is an important diagnostic feature indicating the pathological severity and patient's survival possibility. In this paper, we develop a new method of tracing the brain midline shift in traumatic brain injury (TBI) CT images using its original cause - the hemorrhage. Firstly, we model the relationship between the hemorrhage and the midline deformation caused by it using a linear regression model (H-MLS model). Secondly, using the H-MLS model, the deformed midline is predicted from the hemorrhage detected in CT images. Finally, the predicted deformed midline is adjusted according to the visual symmetry information. Preliminary experiments show that the proposed method is effective and time-efficient.

This paper proposes a scene recognition strategy that integrates the appearance based local SURF features and the geometry based 3D ordinal constraint. Firstly, we show that spatial ordinal ranks of 3D landmarks are well correlated across large camera viewpoint and view direction changes and thus serve as a powerful tool for scene recognition. Secondly, ordinal depth information is acquired in a simple and robust manner when the camera undergoes a bio-mimic ‘Turn-back-and-Look’ TBL) motion. Thirdly, a scene recognition strategy is proposed by combining local SURF feature matches and global 3D rank correlation coefficient into the scene recognition decision process. The performance is validated and evaluated over four indoor and outdoor databases.