Reviewerly/exHarmony huggingface dataset:
Context-Aware Co-attention Neural Network for Service Recommendations

Context-aware recommender systems are able to produce more accurate recommendations by harnessing contextual information, such as consuming time and location.Further, user reviews as an important information source, providing valuable information about users' preferences, items' aspects, and implicit contextual features, could be used to enhance the embeddings of users, items, and contexts.However, few works attempt to incorporate these two types of information, i.e., contexts and reviews, into their models.Recent state-of-the-art context-aware methods only characterize relations between two types of entities among users, items and contexts, which may be insufficient, as the final prediction is closely related to all the three types of entities.In this paper, we propose a novel model, named Context-aware Co-Attention Neural Network (CCANN), to dynamically infer relations between contexts and users/items, and subsequently to model the degree of matching between users' contextual preferences and items' context-aware aspects via coattention mechanism.To better leverage the information from reviews, we propose an embedding method, named Entity2Vec, to jointly learn embeddings of different entities (users, items and contexts) with words in a textual review.Experimental results, on three datasets composed of millions of review records crawled from TripAdvisor, demonstrate that our CCANN significantly outperforms state-of-the-art recommendation methods, and En-tity2Vec can further boost the model's performance.
Functional Organization of Software Groups Considered Harmful

Many software organisations are grouped functionally, i.e., functions such as software engineering, design, product management, etc. are organised into separate groups, with cross-functional teams assembled from across these groups to form product or feature teams. While conducting a Grounded Theory study into the ability of cross-functional software product teams to collectively grok the context of product requirements, we encountered individual and team dynamics that appear to be strongly linked to the software development organisational structure. We argue that an over-arching functional structure of a software development organisation causes cross-functional software product teams to exhibit counter-productive behaviours and that the effectiveness of these cross-functional product or feature teams is significantly compromised as a result of an over-arching functional structure.
Denoising 3D Magnetic Resonance Images Based on Low-Rank Tensor Approximation With Adaptive Multirank Estimation

The magnetic resonance (MR) imaging technique is widely used in clinical diagnosis. Unfortunately, in practice, the MR images inevitably suffer from noise, which severely degrades image quality and accordingly impacts on the accuracy of clinical diagnosis. By exploiting both the nonlocal similarity over space and the inherent correlation across the slices of the 3D MR images, in this paper, we present a novel Rician noise reduction method for the 3D MR images. Specifically, the 3D nonlocal similar patches are first extracted from the input noisy 3D MR data and then grouped to form a noisy fourth-order tensor. Since 3D patches used to construct the fourth-order tensor share similar structures, a latent noise-free tensor can be approximated by a low-rank tensor. To this end, the higher-order singular value decomposition (HOSVD) is adopted to recover the latent noise-free tensor. Furthermore, the rank of each mode of the tensor is adaptively determined by an enhanced low-rank method. The experimental results on synthetic and real 3D MR images show that the proposed method outperforms several state-of-the-art denoising methods in terms of objective metrics and visual inspection.
Supervised Deep Learning Architectures

Many supervised deep learning architectures have evolved over the last few years, achieving top scores on many tasks. Deep learning architectures can achieve high accuracy; sometimes, it can exceed human-level performance. Supervised training of convolutional neural networks, which contain many layers, is done by using a large set of labeled data. Some of the supervised CNN architectures proposed by researchers include LeNet-5, AlexNet, ZFNet, VGGNet, GoogleNet, ResNet, DenseNet, and CapsNet. These architectures are briefly discussed in this chapter.
Source Retrieval Model Focused on Aggregation for plagiarism detection

Source retrieval for plagiarism detection retrieves documents that may be the sources of plagiarism for a suspicious document while minimizing the retrieval costs. Compared with traditional information retrieval, queries from the same suspicious document retain their contextual relations and are no longer isolated from each other. The correlation of queries leads to the aggregation of search results; the documents retrieved by different queries may be relevant or the same. However, previous studies have failed to devote sufficient attention to the aggregation of search results in source retrieval. In this paper, the task of source retrieval is formalized into a framework of learning to rank, and a Ranking Logistical Regression Model is utilized to implement the framework. Furthermore, addressing the aggregation of search results, we propose the Source Retrieval Model Focused on Aggregation for plagiarism detection. We evaluate various aspects of the proposed model on the PAN 2013 and the PAN 2014 Plagiarism Source Retrieval Corpus. With respect to established baselines, the experimental results indicate that the Source Retrieval Model Focused on Aggregation yields statistically significantly improved performance of source retrieval with equal or lower cost effectiveness.
Assessing Personally Perceived Image Quality via Image Features and Collaborative Filtering

During the past few years, different methods for optimizing the camera settings and post-processing techniques to improve the subjective quality of consumer photos have been studied extensively. However, most of the research in the prior art has focused on finding the optimal method for an average user. Since there is large deviation in personal opinions and aesthetic standards, the next challenge is to find the settings and post-processing techniques that fit to the individual users' personal taste. In this study, we aim to predict the personally perceived image quality by combining classical image feature analysis and collaboration filtering approach known from the recommendation systems. The experimental results for the proposed method show promising results. As a practical application, our work can be used for personalizing the camera settings or post-processing parameters for different users and images.
CFB-AES-TURBO: Joint Encryption and Channel Coding for Secure Satellite Data Transmission

As the advanced satellite network can serve extremely diverse information data to different types of users, it is of a vital interest to achieve data security using encryption over satellite channels. However, because the avalanche effect makes encryption techniques vulnerable to bit errors in the wireless channel, a more powerful encryption technique is required. In this paper, we propose a novel method of joint encryption and channel coding, called CFB-AES-TURBO, by combining advanced encryption standard (AES) encryption and turbo coding. As we introduce turbo coding in the cipher feedback (CFB) mode with AES, our scheme can protect bits and also correct bit errors. We control system parameters of the encryption block size and the code rate, tailored to the channel state information (CSI). As a result, we can achieve processing time gains, security enhancement, and bit error rate (BER) performance improvement at the same time.
Metric for Exercise Recognition for Telemedicine Systems

The present paper has considered a task to develop quantitative metrics for automatic recognition of exercise types in the framework of the system for remote monitoring of the physical rehabilitation process. The paper aim is to develop metrics for estimation of physical exercise performance accuracy which allow obtaining quantitative characteristics of the treatment protocol fulfillment accuracy during rehabilitation exercise performance in order to increase objectivity and reliability of the remote control. The paper has suggested to use the algorithm of time scale warping with additional metrics localizing function properties to the minimum warping path. The scientific novelty: method for quantitative estimation of exercise performance correctness remotely based on the comparison of trajectories using the DWT metric has been developed. The present paper has suggested additional metrics that expand the DWT capabilities in the localization of trajectory changes.
Design and Implementation of a Comprehensive Insider Threat Ontology

We describe the development and envisioned use case applications of a comprehensive insider threat ontology—"Sociotechnical and Organizational Factors for Insider Threat" (SOFIT)—that comprises more than 300 indicators of technical, behavioral, and organizational factors. Requirements, design, and engineering development for the Web Ontology Language (OWL) implementation of the SOFIT knowledge base are reviewed; additional relationships among constructs are defined that extend the representation beyond a simple taxonomic hierarchy and that enable inferences for qualitative and quantitative threat assessment. We show how queries may be constructed to support these inferences and threat assessments. Several major application concepts are reviewed to show how the ontology may be used by the insider threat research and operational communities. To this end, the SOFIT knowledge base may be shared with stakeholders to advance research and practice for proactive insider threat mitigation.
Image Enhancement Using Differential Evolution Based Whale Optimization Algorithm

This paper proposes an enhancement method of digital images applying differential evolution based whale optimization algorithm (DEWOA). The enhancement is performed by improving the intensity of pixels in a given image. This is realized using a cost function that contains both the local and global information. A comparison of the proposed DEWOA is performed with few recently developed metaheuristic algorithms like PSO, ABC, CSA, and FPA. The simulation results are presented with respect to the background variance, detail variance, PSNR, entropy, and number of detected edges.
Automated design of tailored link prediction heuristics for applications in enterprise network security

The link prediction problem, which involves determining the likelihood of a relationship between objects, has numerous applications in the areas of recommendation systems, social networking, anomaly detection, and others. A variety of link prediction techniques have been developed to improve predictive performance for different application domains. Selection of the appropriate link prediction heuristic is critical which demonstrates the need for tailored solutions. This work explores the use of hyper-heuristics to automate the selection and generation of customized link prediction algorithms. A genetic programming approach is used to evolve novel solutions from functionality present in existing techniques that exploit characteristics of a specific application to improve performance. Applications of this approach are tested using data from a real-world enterprise computer network to differentiate normal activity from randomly generated anomalous events. Results are presented that demonstrate the potential for the automated design of custom link prediction heuristics that improve upon the predictive capabilities of conventional methods.
A Framework for Maintaining Citizenship Record on Blockchain

The absence of a unified repository of citizens' records hinders strategic planning of government for implementing welfare schemes and service delivery. Keeping a central repository of such records in traditional databases is a security concern as any data breach from the system can compromise the privacy of the citizen. Besides such a system can be misused. In this work, these problems are addressed by developing a secure and robust system for holding the identity records of all the citizens of a nation. In this paper, the capability of Blockchain for acting as a solution to the concerned problem is examined. Blockchain applications inherently possess characteristics of being robust to break down, secured using cryptography and autonomous as they are capable of functioning without the need for manual intervention based on defined business logic. This paper uses the strengths of Blockchain to build a Blockchain-based system that acts as an integrated decentralized database for citizenship records of a nation.
Interactive Context-Aware Anomaly Detection Guided by User Feedback

Automatic anomaly detection techniques have been extensively used to support decision making in abnormal situations. However, existing approaches are limited in their capacity of effectively identifying anomalies due to the complexity of the real-world environment, the uncertainty of the data input, and the unavailability of ground truth. In this paper, we propose an interactive context-aware anomaly detection algorithm framework that incorporates human judgment in searching for anomalous regions within a large geographic environment. In specific, our framework, 1) estimates a focal region and detect anomalous situations in real time, through which the user can observe and analyze suspicious entities, 2) leverages user feedback to refine results and guide further analysis, and 3) tolerates potential fault feedback provided by the users and resignal dubious anomalous points. Based on the framework, we propose two algorithm implementations, respectively, employ Bayes’ theorem and metric learning. We demonstrate the effectiveness of the proposed framework and corresponding implementations through two controlled user studies and a case study with a domain expert.
Automatic Instructional Pointing Gesture Recognition by Machine Learning in the Intelligent Learning Environment

The recorded video in an intelligent learning environment is a key source for analyzing participants' behavior and receiving instructional feedbacks. Teachers and students are the main participants in school education. According to recent studies, the teacher's instructional pointing gesture to the learning content can gain students' attention as well as improve students' learning performance. We aim to automatically recognize these pointing gestures by a series of compound methods. Our main contributions are threefold: first, we collected and labeled our own dataset for instructional pointing gesture recognition in the intelligent learning environment; second, we applied non-linear neural networks to learn the pointing gesture based on the joint points extracted by OpenPose[1]; Third, we proposed a novel framework to recognize the teacher's pointing gesture on a target area, which we named as the instructional pointing gesture, whose contents are frequently changing in real time. From the experimental results, we have proven that our proposed method allows us to recognize the pointing gesture with 90% accuracy on average.
Stochastic resource scheduling via bilayer dynamic Markov decision process in mobile cloud networks

In mobile cloud networks, the efficiency and the quality of service are mainly affected by the service delay, the drop rate of service requests and CPU utilization. For these reasons, we propose a real-time stochastic resource scheduling framework (R-SRSF) to realize pre-allocation of resources by bilayer dynamic markov decision processes (DMDPs). In R-SRSF, we divide the resource scheduling into two parts, the virtual machine (VM) basic configuration resources management and VM switching management. To this end, we firstly design a strategy transformation management algorithm (STMA) to control the VM basic configuration resources. Secondly, we design a scheme-changed drift-plus-penalty algorithm (SDPPA) to execute VM switching management. We also prove the convergence of SDPPA based on Lyapunov optimization theory. For better VM scheduling, we propose a stochastic dynamic policy adjustment algorithm to cooperate with SDPPA. The experimental results show that R-SRSF can reduce the service delay, expenditure and the drop rate of service requests. Moreover, R-SRSF also has a high CPU utilization and stability.
Verified Real Asymptotics in Isabelle/HOL

Interactive theorem provers (or proof assistants) are software with which mathematical definitions and theorems can be formalised. They assist the user in writing formal proofs and check the correctness of these proofs, typically down to the level of basic logical inference steps. This provides a very high degree of assurance that any proof accepted by them is actually sound. Theorem provers contain varying amounts of tools for automation to assist the user, but unlike computer algebra systems, their focus is not on efficient automatic computation.
The Automatic Detection of Sensitive Data in Smart Homes

Smart homes are increasingly becoming popular because they make living comfortable, enjoyable, and secure. People can remotely control various aspects of their smart home environments. However, smart home appliances can pose threats to privacy. The reason is that smart appliances collect and store sensitive information, and if hackers gain access to this information, user privacy may be breached. It is difficult for users to constantly monitor and determine which data is sensitive to them and which one is not. Also, a user’s identity can be leaked during sharing of information with different service providers such as health care providers and utility companies. In this paper we address one important privacy issue in smart homes, which is lack of users’ control over their desired privacy. We propose a privacy decision framework which considers this problem. In this framework, active learning (machine learning) technique is used to help users detect sensitive information according to their privacy preferences.
Forensic Analysis of the Nintendo 3DS NAND

Games consoles present a particular challenge to the forensics investigator due to the nature of the hardware and the inaccessibility of the file system. Many protection measures are put in place to make it deliberately difficult to access raw data in order to protect intellectual property, enhance digital rights management of software and, ultimately, to protect against piracy. History has shown that many such protections on game consoles are circumvented with exploits leading to jailbreaking/rooting and allowing unauthorized software to be launched on the games system. This paper details methods that enable the investigator to extract system activity, deleted images, Internet history items, relevant friends list information, the console's serial number and plaintext WiFi access point passwords. This is all possible with the use of publicly available, open-source security circumvention techniques that perform a non-invasive physical dump of the internal NAND storage of the Nintendo 3DS handheld device. It will also be shown that forensic integrity is maintained and a detailed analysis is possible without altering original evidence.
On the Exact Relationship Between the Success Rate of Template Attack and Different Parameters

In practice, the Template Attack (TA) is widely accepted as the strongest side channel attack, and it is used to evaluate the physical security of crypto devices in practice. In TA, different statistical tools, such as the Euclidean distance (ED) and the Mahalanobis distance (MD), can be used to recover the secret key used by the target device. In practice, values of different parameters influence the key-recovery efficiency of TA. However, until now, the exact relationship between the success rate (SR) of TA and different parameters is not very clear. Therefore, in order to accurately evaluate the physical security of crypto devices with TA, evaluators need to vary values of different parameters and empirically evaluate the SR of TA in different cases. Considering that both the computational complexity and the measurement complexity of the empirical evaluation are high, this may induce unnecessary evaluation overhead and make the evaluation period long. In light of this, in this paper, the exact relationship between the SR of TA and values of different parameters is theoretically analyzed, and the soundness of the theoretical analysis is empirically verified in both simulated and real scenarios. Then, by setting the values of different parameters, the SR of TA can be accurately and efficiently estimated. Overall, this contribution may help evaluators to efficiently evaluate the physical security of crypto devices with TA in practice.
Intelligent System of Strategic Monitoring

The purpose of this paper discusses the role of Soft Computing to adapt dynamically to fast changing environ the long-term strategic design. The role of Soft Computing is help to exploration of the future using Fuzzy Cognitive Maps and Trend Impact Analysis methods, to adapts quickly to major changes in order to reduce the future uncertainty. The conceptual modeling suggests a new choice to anticipate behavior, set of future events and their impact utilizing prior knowledge, to face a dynamic environ. The proposal for integration of Soft Computing and strategic prospective is oriented to develop an Intelligent System of Strategic Monitoring framework.
Topic Tomographies (TopTom): a visual approach to distill information from media streams

Abstract In this paper we present Top Tom, a digital platform whose goal is to provide analytical and visual solutions for the exploration of a dynamic corpus of user‐generated messages and media articles, with the aim of i) distilling the information from thousands of documents in a low‐dimensional space of explainable topics, ii) cluster them in a hierarchical fashion while allowing to drill down to details and stories as constituents of the topics, iii) spotting trends and anomalies. Top Tom implements a batch processing pipeline able to run both in near‐real time with time stamped data from streaming sources and on historical data with a temporal dimension in a cold start mode. The resulting output unfolds along three main axes: time, volume and semantic similarity (i.e. topic hierarchical aggregation). To allow the browsing of data in a multiscale fashion and the identification of anomalous behaviors, three visual metaphors were adopted from biological and medical fields to design visualizations, i.e. the flowing of particles in a coherent stream, tomographic cross sectioning and contrast‐like analysis of biological tissues. The platform interface is composed by three main visualizations with coherent and smooth navigation interactions: calendar view, flow view, and temporal cut view. The integration of these three visual models with the multiscale analytic pipeline proposes a novel system for the identification and exploration of topics from unstructured texts. We evaluated the system using a collection of documents about the emerging opioid epidemics in the United States.
Application of Virtual Reality and Gamification in the Teaching of Art History

The virtual reality (VR) presents a breakthrough in the field of education and others, because it allows you to interact with a fictional world created through the technology. Users can navigate in real time with a subjective perspective of what is there and can be deployed in this three-dimensional world (3D). We use a virtual learning environment (VLE) done in the Unity3D program where you will simulate a virtual museum to enhance the teaching of history of Art. For which we use predefined stages to improve knowledge in an interactive way through gamification techniques testing was conducted on 15 students in higher education, achieving a more active learning.