Reviewerly/exHarmony huggingface dataset:
A Study of Network Forensic Investigation in Docker Environments

Cyber-criminals harness more and more techniques like virtual machines or container-based infrastructures for their malicious activities. The inherent dynamic of these virtual environments simplifies the fast creation of vicious services and hide the involved systems like no other technology before. The primary use of virtualisation and especially containers facilitates software developers and administrators to create new applications, perform tests, debug their code and install pre-defined services based on provided container images. Docker as the most notable container technique provides a great variety of existing container templates, which pave the way for implementing highly dynamic environments. As virtual machines, container-based environments are mostly a short-living on-demand infrastructure, which might be used by cyber-criminals to perform their malicious activities. Especially the virtual layer and the ephemeral nature of the container impede any kind of digital investigation or forensic analysis. In this paper we analyze different methods for network forensic investigation in Docker environments. The virtualisation demands for adapted techniques of packet capture like iptables-manipulation, accessing the internal network bridges or vNICs and the use of software-based techniques. We propose the use of further monitoring processes in Docker swarms to implement a valid packet capture and to collect all relevant network packets. As a result, we define appropriate techniques of packet captures based on parameters of the related container.
Research Status and Prospect for Maritime Object Monitoring Technology

Abstract With the increase of marine and fishery activities, the observation of maritime object has attracted more and more attention. This paper introduced the common methods of object acquisition and image analysis about the maritime object, and then discussed the development trend of image analysis with the deep learning method.
Modular Programming and Reasoning for Living with Uncertainty

Embracing uncertainty in software development is one of the crucial research topics in software engineering. In most projects, we have to deal with uncertain concerns by using informal ways such as documents, mailing lists, or issue tracking systems. This task is tedious and error-prone. Especially, uncertainty in programming is one of the challenging issues to be tackled, because it is difficult to verify the correctness of a program when there are uncertain user requirements, unfixed design choices, and alternative algorithms. If uncertainty can be dealt with modularly, we can add or delete uncertain concerns to/from code whenever they arise or are fixed to certain concerns. This paper proposes a new programming and reasoning style based on Modularity for Uncertainty. The iArch-U IDE (Integrated Development Environment) is developed to support uncertainty-aware software development. The combined usage of a type checker and a model checker in iArch-U plays an important role in verifying whether or not some important properties are guaranteed even if uncertainty remains in a program. Our model checker is based on LTSA (Labelled Transition System Analyzer) and is implemented as an Eclipse plug-in. Agile methods embrace change to accept changeable user requirements. On the other hand, our approach embraces uncertainty to support exploratory software development.
Public Auditing Scheme for Shared Data in the Cloud Storage

Abstract Cloud storage is widely used by both individual users and organizational users due to its efficient and scalable data storage services. Because of the vulnerabilities of hardware/software, human error and malicious attacks, the audit of data integrity in cloud has become the focus of research. However, how to efficiently check the integrity of cloud data and protect the confidentiality of shared data and anonymity of user identity, is still a sharp problem. In this paper, we propose a privacy-preserving mechanism to reduce the security risks in existing schemes, which enables public auditing on shared cloud data. Specifically, identity-based signature is introduced in our scheme, which effectively prevents collusion attack from forgery of signatures. At the same time, the auditors cannot obtain the identity of the signer on shared data. In addition, multiple auditing tasks can be accomplished at one time rather than one by one in our scheme, thus, the efficiency of verification is greatly improved. The theoretical analyses shows that our scheme is correct, secure and privacy protection, and our experiments show that our scheme can efficiently achieve integrity audit of cloud data.
Using Relation Graphs for Improved Understanding of Feature Models in Software Product Lines

Feature models are widely used for describing the variability of a software product line. A feature model contains a tree of features and a set of constraints over these features, which define valid feature combinations. In the industrial practice, large feature models containing hundreds of features and constraints are common. Furthermore, in a hierarchical product line a feature model can be related to other feature models through inter-model constraints. Due to the model size and complexity, understanding industrial feature models is a challenging task.
Applying Semantic Network Analysis to Develop User Experience Assessment Model for Smart TV

The user experience (UX) concept has widely been adopted in the field of development and evaluation of products and services. One reason behind this emerging trend is the limitation in traditional usability evaluation methods (UEMs), which emphasizes performance measures such as task completion time and error rate. To overcome the limitation, users’ perceived value must be set to reflect users’ various experiences. In order to address this issue, this study proposes a network analysis-based method to investigate a value structure to assess the level of user experience in using a product. Gesture interaction on a smart TV is used as a case study to demonstrate the process of obtaining a quantified value structure from the transcriptions of users’ in-depth interview. A major contribution of this study lies in the transformation from qualitative data to a quantified value structure. Product or service designers, in the industrial field, can develop value structure based on network analysis when evaluating their design alternatives within a properly fitted evaluation framework.
Deep Learning Layer Convolutional Neural Network (CNN) Scheme for Cancer Image

Abstract Recent years, in medical image especially cancer detection used whole slide digital scanners, called as histopathology image (images of tissues and cell) where it can now be keep in digital images. Consequently, using Deep Learning will help pathologist in cancer detection (cancer cell known as mitosis). In this paper, we are using Deep Learning Layer Convolutional Neural Network (CNN) for cancer classification using histopathology image and used AMIDA dataset which are related on female breast cancer dataset. Mitosis is an important parameter for the prognosis/diagnosis of breast cancer. However, using histopathology image for cancer detection is a challenging problem that needs a deeper investigations. This problem occurs when to classify mitosis because mitosis is small objects with a large variety of shapes, and they can thus be easily confused with some other objects or artefacts present in the image. In this paper, the objective to find the suitable layer for Deep Learning Convolutional Neural Network and reduce the loss rate.
Deep learning based glance of real world scenes through decision tree

How do computers learn to recognize scene instantly? Humans classify scenes based on local features along with context and also compared with other features. A scene can be described in multiple ways and include details about objects, regions, geometry, location, activities, and even nonvisual attributes. We present a class of efficient streamlined SSD Mobilenet architecture, uses depthwise separate convolution for strong performance on COCO dataset to utilized to recognize real-world objects in Indoor-Outdoor Scene Images. In the scene analysis, the spatial properties are evaluated to generate the final linguistic interpretation and improve classification performance. In this paper, we have proposed an outdoor scene analysis system, by which decision tree rules can be learned to recognize scene categories. The major advantage of this work is by using low-level features, minimal training data, maximum accuracy of prediction analysis. This proposed methodology has been work well to overcome the outdoor scene challenges like lighting conditions and shape complexity. Scene understanding has a great impact on computer vision due to perceiving, analyzing, and interpreting the visual scene which leads to new inventions. In this paper, we present the methodology to recognize the real world scenes in different categories. This approach takes into developing a sequential hierarchy of identifying objects in the scene followed by combining spatial relationships and important scene properties.
Understanding Conditional Compilation through Integrated Representation of Variability and Source Code

The C preprocessor (CPP) is a standard tool for introducing variability into source programs and is often applied either implicitly or explicitly for implementing a Software Product Line (SPL). Despite its practical relevance, CPP has many drawbacks. Because of that it is very difficult to understand the variability implemented using CPP. To facilitate this task we provide an innovative analytics tool which bridges the gap between feature models as more abstract representations of variability and its concrete implementation with the means of CPP. It allows to interactively explore the entities of a source program with respect to the variability realized by conditional compilation. Thus, it simplifies tracing and understanding the effect of enabling or disabling feature flags.
Designing personalized learning environments through monitoring and guiding user interactions with code and natural language

Learning the vocabulary of a new language and a new programming API are similar in multiple ways. In this paper we evaluate several of the similarities and show that based on them we can design systems that can guide the learner towards improving their knowledge without an external tutor or preset curriculum. Instead, the class of systems we propose here are based on automated approaches of building maps of knowledge of the domain by mining repositories. By intersecting this knowledge with models of learner knowledge built by observing past learner interactions with artifacts of the domain we can generate highly personalized learning guidance.
Introducing Self-Adaptive Parameters to Self-organizing Migrating Algorithm

In this paper, a new modification for a modern and popular optimization Self Organizing Migrating Algorithm (SOMA) is presented. SOMA resembles swarm-based algorithms together with mutation process given by perturbation and self-adaptation of individual's migration over the hyperspace of a given optimized solution. However, the quality of the solution found by SOMA strongly depends on user-defined parameters. This is not problematic only for new users, but sometimes for experts as well. The proposed modification allows individual (solution) to change its parameters based on its actual performance and adapts to specific optimization problems. The recent CEC'17 benchmark suite is used for analyzing an original SOMA and performance testing of a proposed SOMA modification. The results are compared and tested for statistical significance.
An Efficient Depth Estimation Technique Using 3-Trait Luminance Profiling

This paper presents an efficient depth estimation technique for depth image-based rendering process in the 3-D television system. It uses three depth cues, namely linear perspective, motion information, and texture characteristics, to estimate the depth of an image. In addition, suitable weights are assigned to different components of the image based on their relative perspective position of either the foreground or the background in the scene. Experimental results on publicly available datasets validate the usefulness of the proposed technique for efficient estimation of depth maps.
Performance analysis for joint processing of pilot and data symbols with channel aging

Considering aging channel effect, the joint processing of pilot and data symbols scheme is investigated. Through the analysis of mutual information, the authors decompose the lower bound of the achievable rate into three terms with explicit physical meanings, and obtain the closed-form expression of the achievable rate. In the derivation of the closed-form expressions, this Letter proposes two novel joint processing algorithms. Through simulation, the joint processing is found to provide a better performance due to the extra information captured by received data signals. In addition, the joint processing scheme is shown to overcome the saturation area issue which further facilitates the application of joint processing. Moreover, the proposed algorithms are shown to enhance the performance gain comparing with the traditional one-step prediction method. The channel aging effect on joint processing is further discussed, remarkably, when the terminal moves fast, the joint processing can still support a relatively satisfying performance.
An FPGA-Based CNN Efficient Storage Processor

At present, deep learning algorithms such as neural networks are widely used in various aspects of artificial intelligence. The computational performance of the CPU is low and the power consumption of the GPU is large. Therefore, this paper studies the implementation of CNN on the FPGA and proposes an effective storage management scheme, which greatly reduces the bandwidth requirement of the operation. The Winograd algorithm is used in the operation to reduce the computational complexity of the convolution, so that the performance of the FPGA is optimized. This design implements Alexnet on the Virtex7 xc7vx690t with 1.32 TFlop/s performance and an average Energy efficiency of 43.5 GOP/s/W.
A Particle Swarm approach to mitigate the apparent diversity-accuracy dilemma in recommendation domains in recommendation domains

Advances in Recommender Systems (RSs) have been focused on improving the system's accuracy. However, accuracy alone is not enough to assess the practical effects. In real scenarios, diversity has been identified as a key dimension of recommendation utility. Thus, the main researches are focused in improve both, accuracy and diversity. This challenge remains an apparent dilemma that remains open and can boost sales by offering consumers both their mainstream and specific tastes. For this reason, we propose an approach to handle the accuracy-diversity dilemma. Our approach, based on a Particle Swarm Optimization (PSO), is a post-processing method to re-rank items from traditional RSs in order to improve diversity without accuracy losses. Experimental results in entertainment and e-commerce scenarios show that our strategy can improve users satisfaction. We improve the diversity up to 70% without significant accuracy losses.
Graph-Based Crime Reports Clustering Using Relations Extracted from Named Entities

The proposed workDas, Priyanka demonstrates a graph-basedDas, Asit Kumar clustering approach for partitioning crime reports using extracted relations from named entities present in a crime corpus of India. The proposed relation extraction scheme initiates with choosing different types of entity pairs followed by computing similarity among the named entity pairs depending on the intermediate context words. A graph (weighted and undirected) is formed with entity pairs representing the nodes and similarity between entities as the weight of the edges linked to the nodes. The clustering coefficient is calculated for all the nodes and the average clustering coefficient has been set as the threshold to partition the graph. Iterative application of the proposed clustering algorithm generates multiple clusters comprising named entity pairs. Finally, the newly formed clusters got labelled according to the most frequent context word of the cluster which results in analysing few aspects of crime performed against women in India. The result with high cluster validation indices illustrates the effectiveness of the proposed method.
Detecting MRSA Infections by Fusing Structured and Unstructured Electronic Health Record Data

Methicillin-resistant Staphylococcus aureus (MRSA), an antibiotic resistant bacteria, is a common cause of one of the more devastating hospital-acquired infections (HAI) in the United States. In this work, we study the practicality of leveraging machine learning methods for early detection of MRSA infections based on a rich variety of patient information commonly available in modern Electronic Health Records (EHR). We explore heterogeneous types of data in EHRs including on-admission demographics, throughout-stay time series and free-form clinical notes. On-admission data capture non-clinical information (e.g., age, marital status) while Throughout-stay data include vital signs, medications, laboratory studies, and other clinical assessments. Clinical notes, free-from text documents created by medical professionals, contain expert observations about patients. Our proposed system generates dense patient-level representations for each data type, extracting features from each of our data types. It then generates scores for each patient, indicating their risk of acquiring MRSA. We evaluate prediction performance achieved by core Machine Learning methods, namely Logistic Regression, Support Vector Machine, and Random Forest, when mining these different types of EHR data retrospectively to detect patterns predictive of MRSA infection. We evaluate classification performance using MIMIC III – a critical care data set comprised of 12 years of patient records from the Beth Israel Deaconess Medical Center Intensive Care Unit in Boston, MA. Our experiments show that while all types of data contain predictive signals, the fusion of all sources of data leads to the most effective prediction accuracy.
Cooperative Optimization Algorithm for the 100-Digit Challenge

As for optimization algorithms, the local optima of multimodal problems always bring difficulties of finding global optimum. This paper proposes a cooperative optimization method that combines differential evolution (DE), univariate sampling, harmony search (HS) and particle swarm optimizer (PSO) to deal with the 100-Digit Challenge of finding global optimum of multimodal problems. The basic strategy is to use some effective algorithms such as DE and univariate sampling to obtain several local optimal solutions, and then to explore a promising search space represented by these local solutions to try to find the global optimum. Experimental tests demonstrate the effectiveness of the proposed cooperative method.
Automatic calibration method for the full parameter of a camera-projector system

We propose an automatic calibration method using grid-structured light for the full parameter of a camera-projector system, including principal points, equivalent focal length, image distortion coefficients, the rotation matrix, and translation vectors between the camera and projector. Grid-structured light is projected onto a board, camera image intersection points are extracted, and three-dimensional intersection points are computed according to a homography matrix. Finally, the full parameter of a camera-projector system is solved based on stereo vision. No manual intervention is required during image processing, which simplifies the operations and improves efficiency. The image-processing kernel problem involves both automatic detection and intersection point matching. (1) The proposed intersection point detection method utilizes multiscale fusion. At each level of the image pyramid, intersection points are searched according to gray distribution and geometrical characteristics. With the gray-gravity method, coordinates are achieved with subpixel intersection point precision. Therefore, the location precision exceeds 0.5 pixel. (2) The proposed matching method employs belief propagation. Taking intersection points as nodes, a Bayesian network is established according to the Markov random field hypothesis. The image intersection point matching problem between a camera and the projector is then transformed into a maximum <italic>a posteriori</italic> estimation problem. Ultimately, 15 images are used to calibrate the full parameter of a camera-projector system. The results indicate that the reprojection error exceeds 0.15 pixel.
Adding Individual Technical Standards to a Library Collection: A Case Study and a Proposed Workflow

Acquiring technical standards connects libraries with vendors who are very protective, legally and technologically, of their products. To conform to the technical restrictions placed on standards, librarians must think creatively and adopt a workflow more labor-intensive than with other publications. This article presents a case study of one institution, a basic discussion of the issues surrounding the addition of technical standards to a library collection, and a stepwise process for the situation in which a patron requests an individual standard.
Warclight: A Rails Engine for Web Archive Discovery

This paper describes the development of Warclight, a portmanteau of the open-source Blacklight platform and the ISO-standard Web ARChive file format. Warclight allows users to explore web archives that have been indexed into Apache Solr using the UK Web Archive's Web Archive Discovery tool. Referencing previous work, we explain how the standard search engine results page is inadequate to support scholarly inquiries. Instead, Warclight provides full-text and faceted search, as well as faceted browsing, to enable exploration and discovery. Given the large sizes of many web archives, we share experiences with deploying our tool at scale using a federated architecture.
HelixMesh

We provide a technical exposition of the HelixMesh protocol and the underlying DAG-based transaction ledger. The HelixMesh is optimized for high-throughput IoT networks on the premise that most transactions carry only a data payload rather than a value transfer. The consensus algorithm is a novel implementation of the hybrid on-/off-chain Mesh-Cash framework based on probabilistic peer sampling for the off-chain layer. We further introduce a flexible "Proof-Of-Contribution" adversarial model which supports both closed permissioned (with standard BFT assumptions) and persmissionless (e.g. based on Proof-Of-Work) networks.
VitaZ: Gamified Mixed Reality Multisensorial lnteractions

This paper presents multiple Mixed Reality 3D interaction, manipulation and simulation techniques in the context of the 2019 3DUI contest of the IEEE VR conference. The proposed schemes provide smart, seamless transition from the real to the virtual world and demonstrate passive haptics, mid-air haptics, object manipulation and abstract entities (time) manipulation. All techniques are integrated in the context of a mixed reality escape-room or treasure-hunt game, where information from both the real and the virtual world is necessary to solve the puzzle. The paper concludes with a discussion on the extensibility and translational application of the approaches in practical problem solving.