Reviewerly/exHarmony huggingface dataset:
An Improved Canny Operator in the Application of Medical Image Segmentation

For the traditional Canny operator has the defects in calculating gradient magnitude and selecting the high and low thresholds manually, an improved Canny operator is proposed, which determines the thresholds by gray histogram and improves the process of calculating gradient magnitude. The improved Canny operator is used to detect the edge of a brain CT image. The experimental results show that the improved algorithm can detect more edge details, effectively suppress false edges, and has stronger adaptability.
Towards Automated Planning for Enterprise Services: Opportunities and Challenges

Existing Artificial Intelligence (AI) driven automation solutions in enterprises employ machine learning, natural language processing, and chatbots. There is an opportunity for AI Planning to be applied, which offers reasoning about action trajectories to help build automation blueprints. AI Planning is a problem-solving technique, where knowledge about available actions and their consequences is used to identify a sequence of actions, which, when applied in a given initial state, satisfy a desired goal. AI Planning has successfully been applied in a number of domains ranging from space applications, logistics and transportation, manufacturing, robotics, scheduling, e-learning, enterprise risk management, and service composition. In this paper, we discuss experience in building automation solutions that employ AI planning for use in enterprise IT and business services, such as change and event management, migration and transformation and RPA composition. We discuss challenges in adoption of AI planning across the enterprise from implementation and deployment perspectives.
Effective Premium Discrimination for Designing Cyber Insurance Policies with Rare Losses

Cyber insurance like other types of insurance is a method of risk transfer, where the insured pays a premium in exchange for coverage in the event of a loss. As a result of the reduced risk for the insured and the lack of information on the insurer’s side, the insured is generally inclined to lower its effort, leading to a worse state of security, a common phenomenon known as moral hazard. To mitigate moral hazard, a widely employed concept is premium discrimination, i.e., an agent/insured who exerts higher effort pays less premium. This, however, relies on the insurer’s ability to assess the effort exerted by the insured. In this paper, we study two methods of premium discrimination that rely on two different types of assessment: pre-screening and post-screening. Pre-screening occurs before the insured enters into a contract and can be done at the beginning of each contract period; the result of this process gives the insurer an estimated risk on the insured, which then determines the contract terms. The post-screening mechanism involves at least two contract periods whereby the second-period premium is increased if a loss event occurs during the first period. Prior work shows that both pre-screening and post-screening are generally effective in mitigating moral hazard and increasing the insured’s effort. The analysis in this study shows, however, that the conclusion becomes more nuanced when loss events are rare. Specifically, we show that post-screening is not effective at all with rare losses, while pre-screening can be an effective method when the agent perceives them as rarer than the insurer does; in this case pre-screening improves both the agent’s effort level and the insurer’s profit.
Writing Tests for This Higher-Order Function First

In functional programming languages, such as Scala and Haskell, a higher-order function is a function that takes one or more functions as parameters or returns a function. Using higher-order functions in programs can increase the generality and reduce the redundancy of source code. To test a higher-order function, a tester needs to check the requirements and write another function as the test input. However, due to the complexity of higher-order functions, testing higher-order functions is a time-consuming and labor-intensive task. Testers have to spend an amount of manual effort in testing all higher-order functions. Such testing is infeasible if the time budget is limited, such as a period before a project release. In this paper, we propose an automatic approach, namely PHOF, which predicts whether a higher-order function will be called in the future. Higherorder functions that are most likely to be called should be tested first. Our approach can assist developers to reduce the number of higherorder functions under test. In PHOF, we extracted 24 features from source code and logs to train a predictive model based on known higher-order functions calls. We empirically evaluated our approach on 2854 higher-order functions from six real-world Scala projects. Experimental results show that PHOF based on the random forest algorithm and the SMOTE strategy performs well in the prediction of calls of higher-order functions. Our work can be used to support the scheduling of limited test resources.
The deep learning word vector model using part of speech and sentiment information

Language Model is used to describe and calculate the probability of a reasonable sentence occurrence in natural language. In practical applications, language model as the core of natural language processing is often used in machine translation, information indexing, voice recognition, context proc essing such as sentiment recognition and other tasks. We will discuss advantages and weaknesses of traditional statistical language models and neural Network Language Models such as CBOW and Skip-gram. Keeping in view the traditional statistical language model and neural network model, we will try to put forward the word vector model based on part of speech and sentiment information (PSWV-model) in order to use more natural language information such as word order features, part of speech features, and sentiment polarity information under the framework of Mikolov’s model. And finally we will present our deliberations on some advantages of PSWV model and other models including CBOW and Skip-Gram, CDNV in the NLP tasks including named entities recognition and sentiment polarity analysis.
3D Reconstruction from RGB-D Data

A key task in computer vision is that of generating virtual 3D models of real-world scenes by reconstructing the shape, appearance and, in the case of dynamic scenes, motion of the scene from visual sensors. Recently, low-cost video plus depth (RGB-D) sensors have become widely available and have been applied to 3D reconstruction of both static and dynamic scenes. RGB-D sensors contain an active depth sensor, which provides a stream of depth maps alongside standard colour video. The low cost and ease of use of RGB-D devices as well as their video rate capture of images along with depth make them well suited to 3D reconstruction. Use of active depth capture overcomes some of the limitations of passive monocular or multiple-view video-based approaches since reliable, metrically accurate estimates of the scene depth at each pixel can be obtained from a single view, even in scenes that lack distinctive texture. There are two key components to 3D reconstruction from RGB-D data: (1) spatial alignment of the surface over time and, (2) fusion of noisy, partial surface measurements into a more complete, consistent 3D model. In the case of static scenes, the sensor is typically moved around the scene and its pose is estimated over time. For dynamic scenes, there may be multiple rigid, articulated, or non-rigidly deforming surfaces to be tracked over time. The fusion component consists of integration of the aligned surface measurements, typically using an intermediate representation, such as the volumetric truncated signed distance field (TSDF). In this chapter, we discuss key recent approaches to 3D reconstruction from depth or RGB-D input, with an emphasis on real-time reconstruction of static scenes.
QoE-fair Resource Allocation for DASH Video Delivery Systems

Services delivering videos to massive audiences are required to provide the users with a satisfactory Quality of Experience (QoE) to keep high engagement and avoid service abandonment. Adaptive BitRate algorithms (ABR) running in video players are designed to dynamically change the video bitrate to provide the best possible QoE given the user device features and the end-to-end network available bandwidth. Well-designed ABR algorithms strive to improve the individual QoE obtained by each user resulting, in the optimal case, in the maximization of the sum of QoE individually perceived by users. However, when resources are scarce, maximizing the sum of the QoE might result in favoring some clients at the expense of others which instead obtain poor QoEs with the possible consequence of service abandonment. Thus, we argue that video service providers should directly address fairness issues when designing their delivery networks so to gracefully degrade the QoE for all users when resources are scarce. This paper addresses this open issue and shows that the Multi-Commodity Flow Problem (MCFP) optimization framework is a proper methodology to achieve a QoE-fair distribution of the resources. The proposed solution is based on the bandwidth reservation approach that slices network resources and assigns similar video requests to the same network slice according to a proposed similarity metric dependent on video quality. Obtained results show that the proposed approach is able to achieve its goal and provide a fair level of QoE to heterogeneous clients.
A Blockchain-based Solution to Fake Check-ins in Location-Based Social Networks

Location-Based Social Networks (LBSNs) are an emerging kind of social network in which users can share their position with others and talk about visited places, providing comments and recommendations. Some LBSNs encourage the voluntary submission of place reviews by offering to users some sort of reward for this activity. However, soon or later this possibility has lead to fraudulent behaviours, in which attackers try to perform fake check-ins in order to increase the obtained rewards without actually visiting any place. Several different solutions have been proposed for distinguishing between real and fake check-ins with a certain degree of confidence. In this paper, we propose an alternative solution based on the use of the emerging blockchain technology, where a decentralized service can provide reliable presence claims for users.
Securing Systems and Software: Current State and Challenges

In modern society, security attribute becomes an essential part of designing and developing software systems. This paper presents different aspects related to designing secure systems and software. The aims to show the importance of the security attribute to cover all possible threads and weakness that may exist in the system. Moreover, a model-based study is conducted to measure the impact of security attribute on other quality attributes, such as performance and usability attributes.
Delayed Best-Fit Task Scheduling to Reduce Energy Consumption in Cloud Data Centers

Reducing energy consumption of cloud data center is critical for its sustainable growth. We propose the delayed best-fit task-scheduling scheme that strategically delays the scheduling of tasks to the most energy-efficient servers of data centers to reduce its energy consumption. The proposed scheme uses static and dynamic thresholds on the power consumption increment an allocated task is associated with an assigned server to balance energy consumption and task completion time. The proposed scheme is tested on a real traffic trace from a Google data center and compared with best-fit and first-fit scheduling algorithms. We show that the proposed delayed best-fit task-scheduling scheme reduces data center energy consumption by 15% of that attained by the best-fit algorithm on the same trace, without compromising the average task completion time.
A unified framework for exploring time-varying volumetric data based on block correspondence

Effective exploration of spatiotemporal volumetric data sets remains a key challenge in scientific visualization. Although great advances have been made over the years, existing solutions typically focus on only one or two aspects of data analysis and visualization. A streamlined workflow for analyzing time-varying data in a comprehensive and unified manner is still missing. Towards this goal, we present a novel approach for time-varying data visualization that encompasses keyframe identification, feature extraction and tracking under a single, unified framework. At the heart of our approach lies in the GPU-accelerated BlockMatch method, a dense block correspondence technique that extends the PatchMatch method from 2D pixels to 3D voxels. Based on the results of dense correspondence, we are able to identify keyframes from the time sequence using k-medoids clustering along with a bidirectional similarity measure. Furthermore, in conjunction with the graph cut algorithm, this framework enables us to perform fine-grained feature extraction and tracking. We tested our approach using several time-varying data sets to demonstrate its effectiveness and utility.
Rayleigh-half normal distribution for modeling tooth movement data

Clear aligners provide a recent technique to induce orthodontic tooth movement. In this paper, a new one-parameter model, called, Rayleigh-Half Normal distribution (RHN) is proposed for modeling tooth movement data. Various statistical and reliability properties including moments and its associated measures, mean residual life, survival and hazard functions are derived and studied. Moreover, the model parameter is estimated via the method of moments and maximum likelihood. A simulation study was performed to examine the bias and mean square error of the estimated parameter. Finally, the flexibility of the proposed model is illustrated by utilizing a real dental data. The results showed that it could give a better ﬁt than other well-known models.
Vertex Codification Applied to 3-D Binary Image Euler Number Computation

A three dimensional (3-D) digital image emerges as a straightforward extension of a two dimensional (2-D) digital image. A 3-D digital image can be obtained by digitizing the 3-D space in which one or more objects of interest can be contained. From each object in the digital image, several features describing their geometry and topology can be computed. One of these features is the Euler number. An alternative method to compute the Euler number of a 3-D digital object (image) in terms of a codification of the vertices of the object voxels is described. The set of formal propositions baseline of the proposal operation are provided, demonstrated and numerically validated with simple objects. Examples with images of different complexity show the applicability of the proposal. The proposed method emerges as an extension of the proposal introduced for the 2-D case in [21] and as alternative of the formulation well described in [22].
Multi-color space learning for image segmentation based on a support vector machine

Image segmentation is one of the most important research directions in computer fields and is also an important component of pattern recognition. In RoboCup competitions, the accurate positioning of the target object is particularly important to the control of the robots. An image segmentation method based on edge and color features is proposed in the present paper. Firstly, the image pixel color features are extracted in multi-color space, and the edge features of the objects are extracted by an adaptive Canny operator. The color features set the entropy, mean, and variance of the pixels in the component image. Then the color and edge features of the image are clustered by Fuzzy C-means. The clustering result of FCM is used as the input of the RBF-SVM model. Finally, the training model is applied to segment the image and output the position of the target object. The present experiment first verified the performance of the multi-color space model by simulation experiments, then conducted image segmentation experiments in images from RoboCup competition, and used Region Circularity and Boundary Center Deviation as evaluation indicators to analyze the performance of the algorithm. The experimental results showed that this method has better performance in image segmentation. Compared with existing methods, it reduced the segmentation time and improved the image segmentation performance.
Heuristics and Metaheuristics Approach for Query Optimization Using Genetics and Memetics Algorithm

Query optimization is one of the important things to make the system can be utilized optimally and minimize the execution time. Heuristics and Metaheuristics Approach using Genetic Algorithm (GA) and Memetics Algorithms (MA) can be used as a way to do optimization. Metaheuristics optimization is developing of the heuristics with applying a local search technique. A Memetics Algorithm (MA) are population-based metaheuristics constitute an extension of the traditional Genetics Algorithm (GA) which is combined with a local search technique. This research will perform the analysis of query optimization using GA and the modified MA which is developing by the combination of GA and Tabu Search (TS) applying on crossover operation. The evaluation is based on the comparison of the execution time of the unoptimized, GA and MA query.
Dynamic Cheap Talk for Robust Adversarial Learning

Robust adversarial learning is considered in the context of closed-loop control with adversarial signaling in this paper. Due to the nature of incomplete information of the control agent about the environment, the belief-dependent signaling game formulation is introduced in the dynamic system and a dynamic cheap talk game is formulated with belief-dependent strategies for both players. We show that the dynamic cheap talk game can further be reformulated as a particular stochastic game, where the states are beliefs of the environment and the actions are the adversarial manipulation strategies and control strategies. Furthermore, the bisimulation metric is proposed and studied for the dynamic cheap talk game, which provides an upper bound on the difference between values of different initial beliefs in the zero-sum equilibrium.
A survey on the use of access permission-based specifications for program verification

Verifying the correctness and reliability of imperative and object-oriented programs is one of the grand challenges in computer science. In imperative programming models, programmers introduce concurrency manually by using explicit concurrency constructs such as multi-threading. Multi-threaded programs are prone to synchronization problems such as data races and dead-locks, and verifying API protocols in object-oriented programs is a non-trivial task due to improper and unexpected state transition at run time. This is in part due to the unexpected sharing of program states in such programs. With these considerations in mind, access permissions have been investigated as a means to reasoning about the correctness of such programs. Access permissions are abstract capabilities that characterize the way a shared resource can be accessed by multiple references. This paper provides a comprehensive survey of existing access permission-based verification approaches. We describe different categories of permissions and permission-based contracts. We elaborate how permission-based specifications have been used to ensure compliance of API protocols and to avoid synchronization problems in concurrent programs. We compare existing approaches based on permission usage, analysis performed, language and/or tool supported, and properties being verified. Finally, we provide insight into the research challenges posed by existing approaches and suggest future directions.
Why Does Data-Driven Beat Theory-Driven Computer Vision?

This paper proposes that despite the success of deep learning methods in computer vision, the dominance we see would not have been possible by the methods of deep learning alone: the tacit change has been the evolution of empirical practice in computer vision. We demonstrate this by examining the distribution of sensor settings in vision datasets, only one potential dataset bias, and performance of both classic and deep learning algorithms under various camera settings. This reveals a strong mismatch between optimal performance ranges of theory-driven algorithms and sensor setting distributions in common vision datasets.
Animating Your Life

We demonstrate a video-to-animation translator, which can transform real-world video into cartoon or ink-wash animation in real-time. When users upload a video or record what they are seeing with the phone, the video-to-animation translator renders the live streaming video with cartoon or ink-wash animation style while maintaining the original contents. We formulate this task as video-to-video translation problem in the absence of any paired training examples, since the manual labeling of such paired video-animation data is cost-expensive and even unrealistic in practice. Technically, an unified unpaired video-to-video translator is utilized to explore both appearance structure and temporal continuity in video synthesis. As such, not only the visual appearance in each frame but also motion between consecutive frames are ensured to be realistic and consistent for video translation. Based on these technologies, our demonstration can be conducted on any videos in the wild and supports live video-to-animation translation, which engages users with the animated artistic expression of their life.
DeepMEF

The goal of video based multi-modal person identification is to identify a person of interest using multi-modal video features, such as person's face, body, audio or head features. This task is challenging due to many factors, for example, variant body or face poses, poor face image quality, low frame resolution, etc. To address these problems, we propose a deep model ensemble framework, namely DeepMEF. Specifically, the proposed framework includes three novel modules, i.e., the video feature fusion module, the multi-modal feature fusion module and the model ensemble module. The first and second module form the basic deep model for ensemble, with the video feature fusion module fuses facial features from different frames as one. Then the multi-modal feature fusion module further fuses the face feature and features of other modalities for identification. In this work, we adopt the scene feature extracted by ourselves as the additional input of the multi-modal module. At last, the model ensemble module promotes the overall performance by combining the predictions of multiple multi-modal learners. The proposed method achieves a competitive result of 89.86% in mAP on the iQIYI-VID-2019 dataset, which helps us win the third place in the 2019 iQIYI Celebrity Video Identification Challenge.