Reviewerly/exHarmony huggingface dataset:
Bing-CF-IDF+: A Semantics-Driven News Recommender System

With the ever growing amount of news on the Web, the need for automatically finding the relevant content increases. Semantics-driven news recommender systems suggest unread items to users by matching user profiles, which are based on information found in previously read articles, with emerging news. This paper proposes an extension to the state-of-the-art semantics-driven CF-IDF+ news recommender system, which uses identified news item concepts and their related concepts for constructing user profiles and processing unread news messages. Due to its domain specificity and reliance on knowledge bases, such a concept-based recommender neglects many highly frequent named entities found in news items, which contain relevant information about a news item's content. Therefore, we extend the CF-IDF+ recommender by adding information found in named entities, through the employment of a Bing-based distance measure. Our Bing-CF-IDF+ recommender outperforms the classic TF-IDF and the concept-based CF-IDF and CF-IDF+ recommenders in terms of the $$F_1$$ -score and the Kappa statistic.
Underwater Object Segmentation Integrating Transmission and Saliency Features

Various types of knowledge and features have been explored for level set-based segmentation. On the ground, the prior knowledge and carefully-designed features perform well to identify the foreground-background contrast, which improves the performance of the segmentation method for complicated and distorted data. However, this is not the case for underwater environments, since the features available on the ground are not suitable for challenging underwater environments. Thus, underwater image segmentation currently lags behind ground-based segmentation. In this paper, novel cues and a suitable model formulation for object segmentation from underwater images are proposed. We consider the special haze effect over underwater images and extract an informative feature (transmission feature) from haze condensation. The saliency feature is also used for underwater object segmentation. Consequently, in our method, the object-background difference can be presented by these features on two levels, i.e., the edge-level transmission and region-level saliency features. These two types of features are integrated into a unified level set formulation to propose a solution that handles the challenging issues in underwater object segmentation. The experimental comparisons of our method with other methods comprehensively demonstrate the satisfactory performance of our method.
Architecture Design of Communication and Backhaul for UAVs in Power Emergency Communication

The power communication network is a dedicated network that serves various application services of the power system, and is the basis for realizing automatic control of the power grid, information management, and intelligent power grid. Among them, the emergency communication network is an important communication means that is indispensable for ensuring the safe and stable operation of the power grid [1]. Since the UAV (unmanned aerial vehicle) often works in harsh environments, the existing data return method is difficult to ensure the smoothness of video information transmission, and the higher the flying height of the UAV, the farther the communication distance, the worse the video transmission quality, so research a communication and data backhaul method for improving the video transmission quality of a cluster UAV has important significance [2], [3]. The purpose of this paper is to provide a communication and data backhaul method [4] for UAVs within the cluster to improve the video transmission quality of the emergency communication network of the power system.
A Cascade of 2.5D CNN and Bidirectional CLSTM Network for Mitotic Cell Detection in 4D Microscopy Image

Mitosis detection is one of the challenging steps in biomedical imaging research, which can be used to observe the cell behavior. Most of the already existing methods that are applied in detecting mitosis usually contain many nonmitotic events (normal cell and background) in the result (false positives, FPs). In order to address such a problem, in this study, we propose to apply 2.5-dimensional (2.5D) networks called CasDetNet_CLSTM, which can accurately detect mitotic events in 4D microscopic images. This CasDetNet_CLSTM involves a 2.5D faster region-based convolutional neural network (Faster R-CNN) as the first network, and a convolutional long short-term memory (CLSTM) network as the second network. The first network is used to select candidate cells using the information from nearby slices, whereas the second network uses temporal information to eliminate FPs and refine the result of the first network. Our experiment shows that the precision and recall of our networks yield better results than those of other state-of-the-art methods.
Generalization Bounds in the Predict-Then-Optimize Framework

The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem and then solve the problem using the predicted values of the parameters. A natural loss function in this environment is to consider the cost of the decisions induced by the predicted parameters in contrast to the prediction error of the parameters. This loss function is referred to as the smart predict-then-optimize (SPO) loss. In this work, we seek to provide bounds on how well the performance of a prediction model fit on training data generalizes out of sample in the context of the SPO loss. Because the SPO loss is nonconvex and non-Lipschitz, standard results for deriving generalization bounds do not apply. We first derive bounds based on the Natarajan dimension that, in the case of a polyhedral feasible region, scale at most logarithmically in the number of extreme points but, in the case of a general convex feasible region, have linear dependence on the decision dimension. By exploiting the structure of the SPO loss function and a key property of the feasible region, which we denote as the strength property, we can dramatically improve the dependence on the decision and feature dimensions. Our approach and analysis rely on placing a margin around problematic predictions that do not yield unique optimal solutions and then providing generalization bounds in the context of a modified margin SPO loss function that is Lipschitz continuous. Finally, we characterize the strength property and show that the modified SPO loss can be computed efficiently for both strongly convex bodies and polytopes with an explicit extreme point representation. Funding: O. El Balghiti thanks Rayens Capital for their support. A. N. Elmachtoub acknowledges the support of the National Science Foundation (NSF) [Grant CMMI-1763000]. P. Grigas acknowledges the support of NSF [Grants CCF-1755705 and CMMI-1762744]. A. Tewari acknowledges the support of the NSF [CAREER grant IIS-1452099] and a Sloan Research Fellowship.
Using Machine Learning Techniques for Evaluating the Similarity of Enterprise Architecture Models

Enterprises Architectures (EA) are facilitated to coordinate enterprise's business visions and strategies successfully and effectively. The practitioners of EA (architects) communicate the architecture to other stakeholders via architecture models. We investigate the scenario where accepted architecture models are stored in a repository. We identified the problem of unnecessary repository expansion by adding model components with similar properties or behavior as already existing repository components. The proposed solution aims to find those similar components and to notify the architect about their existence. We present two approaches for defining and combining similarities between EA model components. The similarity measures are calculated upon the properties of the components and on the context of their usage. We further investigate the behavior of similar architecture models and search for associations in order to obtain components that might be of interest. At the end, we provide a prototype tool for both generating requests and obtaining a result.
Public Immunization Against Complete Subversion Without Random Oracles

We seek constructions of general-purpose immunizers that take arbitrary cryptographic primitives, and transform them into ones that withstand a powerful "malicious but proud" adversary, who attempts to break security by possibly subverting the implementation of all algorithms (including the immunizer itself!), while trying not to be detected. This question is motivated by the recent evidence of cryptographic schemes being intentionally weakened, or designed together with hidden backdoors, e.g., with the scope of mass surveillance. Our main result is a subversion-secure immunizer in the plain model (assuming collision-resistant hashing), that works for a fairly large class of deterministic primitives, i.e., cryptoschemes where a secret (but tamperable) random source is used to generate the keys and the public parameters, whereas all other algorithms are deterministic. The immunizer relies on an additional independent source of public randomness, which is used to sample a public seed. While the public source is untamperable, the subversion of all other algorithms is allowed to depend on it. Previous work in the area only obtained subversion-secure immunization for very restricted classes of primitives, often in weaker models of subversion and relying on random oracles, or by leveraging a higher number of independent random sources.
Aesthetic guided deep regression network for image cropping

Cropping an image to improve its aesthetic quality is a challenging problem because aesthetic, which defines the harmony and beauty in the image, is “really in the eye of the beholder”. Even for the same image, different viewers might have various opinions of optimal composition with respect to aesthetic. To accomplish this subjective task, a deep learning framework is designed where the visual fixations of the image is detected based on selected deep representations and an initial visual saliency rectangle is generated to include the interested objects consequently. Afterwards, a cropping rectangle is proposed by mapping the initial visual saliency bounding box to optimal cropping areas through a regression network, where the relationship between interested objects and the optimal composition of the image is discovered. The experimental results on public datasets show that the proposed method has the competitive results than state-of-the-art approaches.
Recognition of similar characters using gradient features of discriminative regions

One important and challenging issue in handwritten character recognition is the discrimination of visually similar characters. In this paper, we propose a character recognition method for distinguishing similar characters by augmenting commonly used image feature with gradient features from potentially discriminative image regions. The discriminative regions of similar characters sets are automatically detected by analysing the weight vectors of the sparsity promoting logistic fused Lasso method. The histogram of oriented gradients is adopted to compactly represent the gradient features. Additionally, the locality preserving projection method is employed to alleviate the high dimensional nature of the resulting feature vectors. Experimental results on handwritten Lanna Dhamma and Thai characters datasets demonstrate the capability of the proposed method in discriminating visually similar characters. The method also outperforms existing character recognition methods by considerable margins. It has a great potential for character recognition of other alphabets.
Structure-Preserving Stereoscopic View Synthesis With Multi-Scale Adversarial Correlation Matching

This paper addresses stereoscopic view synthesis from a single image. Various recent works solve this task by reorganizing pixels from the input view to reconstruct the target one in a stereo setup. However, purely depending on such photometric-based reconstruction process, the network may produce structurally inconsistent results. Regarding this issue, this work proposes Multi-Scale Adversarial Correlation Matching (MS-ACM), a novel learning framework for structure-aware view synthesis. The proposed framework does not assume any costly supervision signal of scene structures such as depth. Instead, it models structures as self-correlation coefficients extracted from multi-scale feature maps in transformed spaces. In training, the feature space attempts to push the correlation distances between the synthesized and target images far apart, thus amplifying inconsistent structures. At the same time, the view synthesis network minimizes such correlation distances by fixing mistakes it makes. With such adversarial training, structural errors of different scales and levels are iteratively discovered and reduced, preserving both global layouts and fine-grained details. Extensive experiments on the KITTI benchmark show that MS-ACM improves both visual quality and the metrics over existing methods when plugged into recent view synthesis architectures.
Implementation of the Certainty Factor Method for Early Detection of Cirrhosis Based on Android

In the digital era, computers already have an important role in the medical world. One of the roles of the computer is to help diagnose a patient's disease. Cirrhosis is a disease that attacks all parts of the liver with the formation of connective tissue characterized by nodules. Cirrhosis is also a chronic liver disease in which the liver is slowly damaged which lasts for a long time. This damage can expand so that the liver can stop functioning. This condition is called liver failure.
Leveraging Cache Management Hardware for Practical Defense Against Cache Timing Channel Attacks

Sensitive information leakage through shared hardware structures is becoming a growing security concern. In this article, we propose a practical protection framework against cache timing channel attacks by leveraging commercial off-the-shelf hardware support in last level caches for cache monitoring and partitioning.
Participatory Design with Older Adults: Exploring the Latent Needs of Young-Old and Middle-Old in Daily Living Using a Universal Design Approach

In 2017, global population aged 60 years or over reached nearly 963 million, becoming twice the figure recorded in 1980. Not surprisingly ageing population will continue to accelerate due to continuing decline in fertility and improvement in survival in major diseases. When people who are suffered from cognitive or physical impairment, they often feel alone and experience different degrees of social loneliness. This paper discusses co-design experiences with various stakeholders to explore latent needs of older persons in their daily living using a universal design approach. Through iterative use of creative methods, freehand sketching and physical models, older adults can express their needs in a more accurate, tactile format. Findings reveal that commonality of interest among older persons are important in building rapport among other participants. It also helps designers develop assistive design related to health care, mobility, dining and leisure activities involving older persons, benefiting society as a whole.
CATAPULT

Visual graph query interfaces (a.k.a gui ) widen the reach of graph querying frameworks across different users by enabling non-programmers to use them. Consequently, several commercial and academic frameworks for querying a large collection of small- or medium-sized data graphs (\textite.g., chemical compounds) provide such visual interfaces. Majority of these interfaces expose a fixed set ofcanned patterns (\textiti.e., small subgraph patterns) to expedite query formulation by enabling pattern-at-a-time in lieu of edge-at-a-time construction mode. Canned patterns to be displayed on a gui are typically selected manually based on domain knowledge. However, manual generation of canned patterns is labour intensive. Furthermore, these patterns may not sufficiently cover the underlying data graphs to expedite visual formulation of a wide range of subgraph queries. In this paper, we present a generic and extensible framework called Catapult to address these limitations. Catapult takes a data-driven approach toautomatically select canned patterns, thereby taking a concrete step towards the vision of data-driven construction of visual query interfaces. Specifically, it firstclusters the underlying data graphs based on their topological similarities and thensummarize each cluster to create acluster summary graph (csg ). The canned patterns within a user-specifiedpattern budget are then generated from these csg s by maximizingcoverage anddiversity, and minimizingcognitive load of the patterns. Experimental study with real-world datasets and visual graph interfaces demonstrates the superiority of Catapult compared to traditional techniques.
Exploring High-Order Correlations for Industry Anomaly Detection

Anomaly detection aims to find the outlier noise data, which has attracted much attention in recent years. However, in most industrial cases, we can only obtain very few labeled anomalous data with abundant unlabeled data, which makes anomaly detection intractable. To tackle this issue, in this paper we propose a vertex-weighted hypergraph learning method for anomaly detection. In this method, the correlation among data is formulated in a hypergraph structure, where each vertex denotes one sample, and the connections (hyperedges) on the hypergraph represent the relation among samples in feature space. We introduce vertex weights to investigate the importance of different samples, where each vertex is initialized with a weight corresponding to its similarity score and isolation score. Then, we learn the label projection matrix for anomaly detection and optimize vertex weights simultaneously. In this way, the vertices with high weights play an important role during the learning process. We have evaluated our method on industry anomaly detection dataset, outlier detection dataset, and software defect prediction dataset, and experimental results show that our method achieves better performance when compared with other state-of-the-art methods.
Robot Mission Planning using Co-evolutionary Optimization

Summary Mission planning is a complex motion planning problem specified by using Temporal Logic constituting of Boolean and temporal operators, typically solved by model verification algorithms with an exponential complexity. The paper proposes co-evolutionary optimization thus building an iterative solution to the problem. The language for mission specification is generic enough to represent everyday missions, while specific enough to design heuristics. The mission is broken into components which cooperate with each other. The experiments confirm that the robot is able to outperform the search, evolutionary and model verification techniques. The results are demonstrated by using a Pioneer LX robot.
PIClean

With the dramatic increasing interest in data analysis, ensuring data quality becomes one of the most important topics in data science. Data Cleaning, the process of ensuring data quality, is composed of two stages: error detection and error repair. Despite decades of research in data cleaning, existing cleaning systems still have limitations in terms of usability and error coverage. We propose PIClean, a probabilistic and interactive data cleaning system that aims at addressing the aforementioned limitations. PIClean produces probabilistic errors and probabilistic fixes using low-rank approximation, which implicitly discovers and uses relationships between columns of a dataset for cleaning. The probabilistic errors and fixes are confirmed or rejected by users, and the user feedbacks are constantly incorporated by PIClean to produce more accurate and higher-coverage cleaning results.
<i>Retracted</i>: Traffic identification and traffic analysis based on support vector machine

Summary The number of applications based on the Internet is increasing, which results the traffic becoming more and more complex. Therefore, how to improve the service quality and security of the network is becoming more and more important. This paper studies the application of SVM in traffic identification to classify network traffic. Through data collection and feature generation methods and network traffic feature screening methods, SVM is used as a classifier by using the generalization capability of SVM, and the parameters and kernel functions of the SVM are adjusted and selected based on cross comparison ideas and methods. Using the cross‐validation method to make the most reasonable statistics for the classification and recognition accuracy of the adjusted support vector machine avoids the situation that the classification accuracy of the support vector machine is unstable or the statistics are inaccurate. Finally, a traffic classification and identification system based on SVM is realized. The final recognition rate of encrypted traffic is up to 99.31%, which overcomes the disadvantages of traditional traffic identification and achieves a fairly reliable accuracy.
Securix: a 3D game-based learning approach for phishing attack awareness

Majority of Internet users lack anti-phishing skills in identifying a phishing attack. This paper introduces a 3D-game prototype named Securix which simplifies and exaggerates real life scenario and conveys different game design principles. Securix is divided into three levels, depicted in scenarios, namely: URL, E-mail and Website which addresses different types of phishing attack. 3D characters and tools were designed to be imported into the game engine using C# programming language for scripting. A Technology Acceptance Model was used for evaluation of the game. To ascertain the liability and acceptability of this design, 50 questionnaires were administered. The results revealed perceived usefulness as the most significant determinant of adoption of Securix than all the other variables. All the relationships between Perceived Ease Of Use (PEOU), Perceived Usefulness (PU), Attitude Towards Using (ATU), and Actual Usage of the System (AUOS) were tested and found to be significant and positive. Analysis from the questionnaire revealed that PU was a strong predictor of actual usage with ninety-five percent (95%) of users as compared to PEOU which was sixty-eight percent (68%) and AUOS which had a result of seventy-four percent (74%). The overall game design enhances the user’s avoidance behaviour through motivation to protect themselves against phishing threats.
ARCat

In this paper we present ARCat, a tangible programming tool designed to help children learn Depth First Search (DFS) algorithm with augmented reality (AR) technology. With this tool, children could use tangible programming cards to control a search process, rather than control virtual characters directly. With the special design of card semantics and real-time feedback, the cognitive load of the learning process had been proved to be affordable to children (ages 8-9) with the result of our preliminary evaluation, which shows the possibility of basic algorithm education for young children with tangible interface.