Reviewerly/exHarmony huggingface dataset:
Secure Automated Forensic Investigation for Sustainable Critical Infrastructures Compliant with Green Computing Requirements

SCADA (Supervisory Control and Data Acquisition) networks are built to efficiently provide supervisory and control of national and international critical infrastructures. SCADA networks represent a challenging domain for forensic investigators who have the responsibility to discover the main causes of the catastrophic incidents that could happen in these critical mission systems and provide precise and logical evidences supported with comprehensive technical reports to the legal organizations. They urgently need technological tools and frameworks that enable them to effectively do their mission without affecting the running state of SCADA networks which must be sustainable and robust against technical and disruptive incidents. This paper discusses the challenges and opportunities towards achieving that goal and highlights the emerging technological approaches and paradigms that can be considered as promising for the realization of such a framework taking into account the efficient consumption of computational resources. Further, this paper proposes a conceptual framework for automated and secure forensic investigation in modern complex SCADA networks accompanied with a possible realization architecture based on the Multi-Agent Systems (MAS) and Wireless Sensor Networks (WSN) promising technological paradigms. The proposed framework is intentionally designed to be compliant with the currently active motivation towards promoting green computing requirements.
Multi-modal Bayesian embedding for point-of-interest recommendation on location-based cyber-physical–social networks

Point-of-interest (POI) recommendation has become a favorite topic on location-based cyber-physical–social networks (LBCPSNs). The geographical influence of locations (e.g., geographical proximity) and the social influence of friends (e.g., social ties) have been widely used in many existing POI recommendation methods, most of which paid little attention to the sequential influence and temporal influence of locations on users' check-in behaviors. However, human mobility exhibits sequential and temporal patterns. In this paper, we propose a unified probabilistic generative model, i.e., a multi-modal Bayesian embedding model (MMBE), to discover the social, sequential, temporal, and spatial patterns of users' check-in behaviors simultaneously. Besides, MMBE can model the joint effect of the four factors mentioned above on the decision-making for POI selection across heterogeneous workflow activities in cyber-physical–social(CPS) systems. Then, we conduct an in-depth performance evaluation for MMBE using two large-scale real-world datasets from Gowalla and Brightkite. Experimental results show that our proposed method outperforms the other five state-of-the-art POI recommendation approaches regarding Precision and Recall.
An Availability Analysis Approach for Deployment Configurations of Containers

Operating system (OS) containers enabling the microservice-oriented architecture are becoming popular in the context of Cloud services. Containers provide the ability to create lightweight and portable runtime environments that decouple the application requirements from the characteristics of the underlying system. Services built on containers have a small resource footprint in terms of processing, storage, memory and network, allowing a more dense deployment environment. While the performance of such containers is addressed in few previous studies, understanding the failure-repair behavior of the containers remains unexplored. In this paper, from an availability point of view, we propose and compare different configuration models for deploying a containerized software system. Inspired by Google Kubernetes, a container management system, these configurations are characterized with a failure response and migration service. We develop novel non-state-space (i.e., fault tree) and state-space (i.e., stochastic reward net) analytic models for container availability analysis. Analytical as well as simulative solutions are obtained for the developed models. Our analysis provides insights on k out-of N availability and sensitivity of system availability for key system parameters. Finally, we build an open-source software tool powered by these models. The tool helps a Cloud administrator to assess the availability of a containerized system and to conduct a what-if analysis based on user-provided parameters and configurations.
Machine Learning and Evolutionary Techniques in Interplanetary Trajectory Design

After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission. The results, limited to the chosen test case of an Earth–Mars orbital transfer, extend the findings made previously for landing scenarios and quadcopter dynamics, opening a new research area in interplanetary trajectory planning.
Efficient Video Object Co-Localization With Co-Saliency Activated Tracklets

Video object co-localization is the task of jointly localizing common visual objects across videos. Due to the large variations both across the videos and within each video, it is quite challenging to identify and track the common objects jointly. Unlike the previous joint frameworks that use a large number of bounding box proposals to attack the problem, we propose to leverage <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">co-saliency activated tracklets</i> to efficiently address the problem. To highlight the common object regions, we first explore inter-video commonness, intra-video commonness, and motion saliency to generate the co-saliency maps for a small number of selected key frames at regular intervals. Object proposals of high objectness and co-saliency scores in those frames are tracked across each interval to build tracklets. Finally, the best tube for a video is obtained through selecting the optimal tracklet from each interval with the help of confidence and smoothness constraints. Experimental results on the benchmark YouTube-objects dataset show that the proposed method outperforms the state-of-the-art methods in terms of accuracy and speed under both weakly supervised and unsupervised settings. Moreover, by noticing the existing benchmark dataset lacks of sufficient annotations for object localization (only one annotated frame per video), we further annotate more than 15k frames of the YouTube videos and develop a new benchmark dataset for video co-localization.
Robust Optimization Over Time by Learning Problem Space Characteristics

Robust optimization over time is a new way to tackle dynamic optimization problems where the goal is to find solutions that remain acceptable over an extended period of time.The state-of-the-art methods in this domain try to identify robust solutions based on their future predicted fitness values.However, predicting future fitness values is difficult and error prone.In this paper, we propose a new framework based on a multi-population method in which sub-populations are responsible for tracking peaks and also gathering characteristic information about them.When the quality of the current robust solution falls below the acceptance threshold, the algorithm chooses the next robust solution based on the collected information.We propose four different strategies to select the next solution.The experimental results on benchmark problems show that our newly proposed methods perform significantly better than existing algorithms.
Secure and Efficient Skyline Queries on Encrypted Data

Outsourcing data and computation to cloud server provides a cost-effective way to support large scale data storage and query processing. However, due to security and privacy concerns, sensitive data (e.g., medical records) need to be protected from the cloud server and other unauthorized users. One approach is to outsource encrypted data to the cloud server and have the cloud server perform query processing on the encrypted data only. It remains a challenging task to support various queries over encrypted data in a secure and efficient way such that the cloud server does not gain any knowledge about the data, query, and query result. In this paper, we study the problem of secure skyline queries over encrypted data. The skyline query is particularly important for multi-criteria decision making but also presents significant challenges due to its complex computations. We propose a fully secure skyline query protocol on data encrypted using semantically-secure encryption. As a key subroutine, we present a new secure dominance protocol, which can be also used as a building block for other queries. Furthermore, we demonstrate two optimizations, data partitioning and lazy merging, to further reduce the computation load. Finally, we provide both serial and parallelized implementations and empirically study the protocols in terms of efficiency and scalability under different parameter settings, verifying the feasibility of our proposed solutions.
Converted Measurement Sigma Point Kalman Filter for Bistatic Sonar and Radar Tracking

Tracking with bistatic sonar or radar measurements is challenging due to the fact that the measurements are nonlinear functions of the Cartesian state. The performance of existing approaches, including the extended Kalman filter and sigma point Kalman filters, such as the unscented Kalman filter and cubature Kalman filter, may not be acceptable in terms of mean square error or tracker consistency (i.e., the tracker's state estimation error covariance is not statistically consistent with the actual estimation errors). This paper generalizes the sigma point Kalman filter (SPKF) as a converted measurement SPKF. The resulting converted measurement sigma point Kalman filter is demonstrated to have improved performance over the conventional SPKF when employed in a bistatic tracking scenario.
Text-image watermarking based on integer wavelet transform (IWT) and discrete cosine transform (DCT)

Text-images still have a great importance, despite the spread of electronic texts. Text-images are protected using digital watermarking. This paper proposes a watermarking method applied to text-images. This method integrates two transforms: integer wavelet transform (IWT) and discrete cosine transform (DCT). For the watermark embedding, IWT is performed on the cover image and DCT is applied to the low frequency sub-band LL. The watermark image is inserted in the lower to medium DCT coefficients to achieve a high degree of robustness and imperceptibility. Experiments using different Arabic text-images are performed to evaluate the proposed method in terms of imperceptibility and robustness. Results show that the proposed method has higher imperceptibility compared to other existing methods applied to Arabic text-images. Also, the proposed method provides good robustness results, especially against compression and noise.
Multi-task learning using variational auto-encoder for sentiment classification

With the rapid growth of the big data, many approaches in the representation of text for sentiment classification have been successfully proposed in natural language processing. However, these approaches remedy this problem based on single-task supervised objectives learning and do not consider their relative of multiple tasks. Based on these defects, in this work, we consider these tasks are relative, and use weight-shared parameters for learning the representation of text in neural network model, we introduce and study a multi-task approach with variational auto-encoder generative model (MTVAE) by jointly learning them. Experimental results on six subsets of Amazon review data show that the proposed approach can effectively improve the sentiment classification accuracy by other relative tasks.
Multi-Label Learning from Crowds

We consider multi-label crowdsourcing learning in two scenarios. In the first scenario, we aim at inferring instances' groundtruth given the crowds' annotations. We propose two approaches NAM/RAM (Neighborhood/Relevance Aware Multi-label crowdsourcing) modeling the crowds' expertise and label correlations from different perspectives. Extended from single-label crowdsourcing methods, NAM models the crowds' expertise on individual labels, but based on the idea that for rational workers, their annotations for instances similar in the feature space should also be similar, NAM utilizes information from the feature space and incorporates the local influence of neighborhoods' annotations. Noting that the crowds tend to act in an effort-saving manner while labeling multiple labels, i.e., rather than carefully annotating every proper label, they would prefer scanning and tagging a few most relevant labels, RAM models the crowds' expertise as their ability to distinguish the relevance between label pairs. In the second scenario, we care about cost-efficient crowdsourcing where the labeling and learning process are conducted in tandem. We extend NAM/RAM to the active paradigm and propose instance, label, and worker selection criteria such that the labeling cost is significantly saved compared to passive learning without labeling control. The proposals' effectiveness are validated on simulated and real data.
You Can Access but You Cannot Leak: Defending Against Illegal Content Redistribution in Encrypted Cloud Media Center

The wide adoption of cloud greatly facilitates the sharing of explosively generated media content today, yet deprives content providers' direct control over the outsourced media content. Thus, it is pivotal to build an encrypted cloud media center where only authorized access is allowed. Enforcing access control alone, however, cannot fully protect content providers' interests, as authorized users may later become traitors that illegally redistribute media content to the public. Such realistic threat should have been seriously treated yet is largely overlooked in the literature. In this paper, we initiate the first study on secure media sharing with fair traitor tracing in the encrypted cloud media center, through a new marriage of proxy re-encryption (for secure media sharing) and fair watermarking (for fair traitor tracing). Our key insight is to fully leverage the homomorphic properties residing in proxy re-encryption to embrace operations in fair watermarking. Two protocols are proposed for different application scenarios. We also provide complexity analysis for performance, showing that our work can also be treated as secure outsourcing of fair watermarking, and thus kills two birds with one stone. We thoroughly analyze the security strengths and conduct extensive experiments to validate the effectiveness of our design.
Complex Activity Recognition Using Acceleration, Vital Sign, and Location Data

Complex activity recognition is a valuable issue in mobile and wearable computing. Since complex activities are strongly relevant to users' locations, location data can be used in complex activity recognition. Current activity recognition methods utilizing location data either do not make the most of location data or have a “cold-start” problem. In this paper, we propose CAROLINA (complex activity recognition using acceleration, vital sign, and location data) to recognize complex activities from wearable sensors. CAROLINA divides a map into grid cells and builds a grid cell-activity matrix by integrating check-in and ATUS (American Time Use Survey) datasets. Since some grid cells do not have check-in records, there are some null entries in the matrix, and all the entries are reconstructed by matrix factorization. Meanwhile, a POI (point of interest) category dataset is exploited to build a grid cell-grid cell similarity matrix, which is used to reduce the loss of matrix factorization. The results of experiments show that CAROLINA can effectively utilize location data to improve complex activity recognition performance.
On an algorithm for human action recognition

Human action recognition which needs video processing in real time, requires large memory size and execution time. This work proposes a local maxima of difference image (LMDI) based interest point detection technique, random projection tree with overlapping split and modified voting score for human action recognition. In LMDI based interest point detection method, difference images are obtained using consecutive frame differencing technique and next, 3D peak detection is applied on the bunch of calculated difference images. Histogram of oriented gradients and histogram of optical flow as local features are extracted by defining a block of size 16 × 16 around each of the interest point. These local features are then indexed by random projection trees. Overlapping split is used during tree structuring to reduce failure probability. Hough voting technique is applied on testing video to compute highest similarity matching score with individual training classes. In addition to Hough voting score, the number of matched interest points of a single query video with each training class, is considered for recognition. The proposed method is evaluated on segmented UT-interaction dataset, J-HMDB dataset and UCF101 dataset. The experimental results indicate that the proposed technique provides better performance compared to earlier reported techniques.
Dual-verification network for zero-shot learning

To mitigate the problems of visual ambiguity and domain shift in conventional zero-shot learning (ZSL), in this paper, we propose a novel method, namely, dual-verification network (DVN), which accepts features and attributes in a pairwise manner as input and verifies the result in both the attribute and feature spaces. First, the DVN projects a feature onto an orthogonal space, where the projected feature has maximum correlation with its corresponding attribute and is orthogonal to all the other attributes. Second, we adopt the concept of semantic feature representation, which computes the relationship between the semantic feature and class labels. Based on this concept, we project the attributes onto the feature space by extending the attributes and labels from the class level to instance level. In addition, we employ a deep architecture and utilize the cross entropy loss to train an end-to-end network for dual verification. Extensive experiments in ZSL and generalized ZSL are performed on four well-known datasets, and the results show that the proposed DVN exhibits a competitive performance relative to the state-of-the-art methods.
SIRIUS: Dual, Symmetric, Interactive Dimension Reductions

Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights.
Clustering by twin support vector machine and least square twin support vector classifier with uniform output coding

The recently proposed twin support vector clustering (TWSVC) is a powerful clustering method. However, TWSVC may encounter the singularity problem and it is time consuming in the learning stage. In this paper, we introduce some efficient techniques into TWSVC, and propose two clustering models, called twin bounded support vector clustering (TBSVC) and least square twin bounded support vector clustering (LSTBSVC), respectively. TBSVC introduces a maximum margin regularization term into TWSVC, which not only avoids its singularity but also significantly improves the performance. LSTBSVC introduces the least square formation into TBSVC to greatly accelerate its learning speed. Moreover, a uniform output coding for LSTBSVC is introduced to cope with the non-uniformed problem in the learning procedures. In addition, nonlinear clustering is also extended to the above clustering methods by using the kernel trick. Experimental results show the effectiveness and efficiency of our methods.
Software Configuration Engineering in Practice Interviews, Survey, and Systematic Literature Review

Modern software applications are adapted to different situations (e.g., memory limits, enabling/disabling features, database credentials) by changing the values of configuration options, without any source code modifications. According to several studies, this flexibility is expensive as configuration failures represent one of the most common types of software failures. They are also hard to debug and resolve as they require a lot of effort to detect which options are misconfigured among a large number of configuration options and values, while comprehension of the code also is hampered by sprinkling conditional checks of the values of configuration options. Although researchers have proposed various approaches to help debug or prevent configuration failures, especially from the end users' perspective, this paper takes a step back to understand the process required by practitioners to engineer the run-time configuration options in their source code, the challenges they experience as well as best practices that they have or could adopt. By interviewing 14 software engineering experts, followed by a large survey on 229 Java software engineers, we identified 9 major activities related to configuration engineering, 22 challenges faced by developers, and 24 expert recommendations to improve software configuration quality. We complemented this study by a systematic literature review to enrich the experts' recommendations, and to identify possible solutions discussed and evaluated by the research community for the developers' problems and challenges. We find that developers face a variety of challenges for all nine configuration engineering activities, starting from the creation of options, which generally is not planned beforehand and increases the complexity of a software system, to the non-trivial comprehension and debugging of configurations, and ending with the risky maintenance of configuration options, since developers avoid touching and changing configuration options in a mature system. We also find that researchers thus far focus primarily on testing and debugging configuration failures, leaving a large range of opportunities for future work.