Reviewerly/exHarmony huggingface dataset:
Cross Modality Guided Liver Image Enhancement of CT Using MRI

Low contrast Computed Tomographic (CT) images often hamper the diagnosis of critical tumors found in various human organs. Contrast enhancement schemes play significant role in improving the visualization of these structures. To achieve this objective, Crossmodality Guided Enhancement (CMGE) method is proposed in this paper. The idea is to exploit the diversity of the information extracted from one modality to enhance the important structures including vessels and tumors in another modality. Our method employs information from liver Magnetic Resonance Image (MRI) to generate an enhanced CT image. It entails applying two dimensional histogram specification to map 2D histogram of CT to that of MRI followed by application of top and bottom hat transformations. These morphological operations highlight areas brighter than their surroundings and suppress darker areas. The final image is obtained by combining the results of these operations. Our method is compared with other state of the art contrast enhancement methods both visually and in terms of quality assessment metrics IEM and EME. The results show that our method performs better than these methods. CMGE technique yields improved contrast in low contrast CT images of the human liver and highlights tumors and vessels.
Improved Ternary Reversible Logic Synthesis Using Group Theoretic Approach

Quantum computation relies on exploiting quantum mechanical phenomena, and has received significant attention in recent years. Higher-dimensional quantum systems increase the density of encoded information per computing element (e.g., qutrit for three-level system), resulting in less resource overhead. For instance, 63% reduction in the number of qutrits is possible for ternary quantum systems as compared to the corresponding binary systems. The proposed work exploits this fact to synthesize ternary reversible circuits employing a cycle-based technique. The method starts from the ternary reversible specification of a given function in the form of a permutation. The permutation cycles are factored into simpler three-cycles and two-cycles, which are then mapped to ternary reversible gates. Different gate libraries are used to synthesize three-cycles and two-cycles, respectively. A gate decomposition approach is also proposed to synthesize a quantum gate netlist in terms of elementary ternary quantum gates, viz. Muthukrishnan–Stroud gate and shift gate. Synthesis results on benchmark functions indicate that the proposed method results in 27% and 6% improvements in quantum cost and gate count, respectively, over existing works in the literature.
Cognisance as a Human Factor in Military Cyber Defence Education

Cyber Defence Exercises (CDX) are common training and learning tools. A recently discussed challenge in cyber defence teaching and training is the gap between the fast technological advancement accompanied by rapidly changing demands on future cyber defence operators, and the lack of science-based teaching and training methods. A growing body of evidence suggests a crucial role of human factors as a central predictor for human performance in sociotechnical systems. While this has been acknowledged in a wide range of safety-critical applied fields, there is still a lack of knowledge about the impact of human factors on cyber defence performance. The lack of conventional metrics of performance and learning progress contribute to this deficit. To address this gap, the Norwegian Defence Cyber Academy (NDCA) follows a science-based educational approach that identified in a series of empirical studies cognitive-psychological predictors for learning success of future cyber defence operators. These predictors and elements of a human factors research program are deeply embedded into educational practice and include processes such as metacognition, self-regulation, coping, communication and shared mental modelling. Slow education methods and mentoring are fundamental to enabling the advancement of human factors cognisance among military cyber cadets. As a tool for efficient training, the NDCA developed and implemented a mentoring concept that involves a cyber defence retrospective timeline analysis involving expert and practitioner level mentors. The timeline differentiates between performance relevant hard- and soft-skills and leads progressively towards an alignment of Security Operation Centre (SOC)- and expert judgments of performance. The NDCA argues that this educational concept facilitates educational benefits based on insight, accurate self-perception, motivation and decreased team workloads following more efficient collaboration.
Analysis of a blackbody irradiance method of measurement of solar blind UV cameras' sensitivity

This paper presents a critical analysis of a current typical method to measure sensitivity of solar blind ultraviolet cameras using a high temperature blackbody as a calibrated source of ultraviolet light.It has been shown that measurement of sensitivity of solar-blind ultraviolet (SBUV) cameras defined as minimal detectable blackbody irradiance at optics plane of the tested SBUV camera generates inflated, misleading and prone to measurement errors' results that should not be used for evaluation of SBUV cameras' performance.
Rethinking Community in Linguistics: Language and Community in the Digital Age

Within the field of linguistics, sociolinguistics has a long-standing tradition of providing highly relevant contributions to community research, in particular with its work on speech communities and communities of practice. While 'speech communities' are groups with shared values and attitudes towards language use, the term 'community of practice' emphasises the idea of shared goals and practices in a community. Sven Leuckert's contribution links these traditional notions of sociolinguistics to the digital realm by reporting on a case study using data from the social media platform Reddit, a major website for online communities. In order to establish that subreddits, i.e. specialised sub-forums on Reddit, are indeed (linguistic) communities, linguistic features shared by users on the website and meta-comments about the community are presented and discussed. A central outcome of the case study is that online communities have much in common with traditional ideas of communities, but they also offer new challenges and insights for sociolinguistics and community research in a wider context. In particular, the degree of freedom and accessibility as well as the blurring of modes of communication in online communities require in-depth analysis.
Endpoint Protection: Measuring the Effectiveness of Remediation Technologies and Methodologies for Insider Threat

With the increase in the incidences of data leakage, enterprises have started to realize that the endpoints (especially mobile devices) used by their employees are the primary cause of data breach in most of the cases. Data shows that employee training, which aims to promote the awareness of protecting the sensitive data of the organization is not very useful. Besides, popular third-party cloud services make it even more difficult for employees to keep the secrets of their workplace safer. This pressing issue has caused the emergence of a significant market for various software products that provide endpoint data protection for these organizations. Our study will discuss some methods and technologies that deal with traditional, negative endpoint protection: Endpoint protection platform (EPP), and another new, positive endpoint protection: Endpoint detection and response (EDR). The comparison and evaluation between EPP and EDR in mechanism and effectiveness will also be shown. The study also aims to analyze the merits, faults, and key features that an excellent protection software should have. The objective of this paper is to assist small-scale and big-scale companies to improve their understanding of insider threats in such rapidly developing cyberspace, which is full of potential risks and attacks. This will also help the companies to have better control over their employee's endpoint to be able to avoid any future data leaks. It will also help negligent users to comprehend how serious is the problem that they are faced with, and how they should be careful in handling their privacy when they are surfing the Internet while being connected to the company's network. This paper aims to contribute to further research on endpoint detection and protection or some similar topics by trying to predict the future of protection products.
Pose and Expression Robust Age Estimation via 3D Face Reconstruction from a Single Image

In this paper, we present a deep learning architecture that exploits 3D face reconstruction to obtain a robust age estimation. To this end, effective representation is learned through an expression-, pose-, illumination-, reflectance-, and geometry-aware deep model reconstructing a 3D face from a single 2D image. The 3D face reconstruction network is combined with an appearance-based age estimation network, where the face reconstruction features are jointly learned with the visual ones. Experiments on large-scale datasets show that our method obtains promising results and outperforms state-of-the-art methods, especially in the presence of strong expressions and large pose variations. Furthermore, cross-dataset experiments show that the proposed method is able to generalize more effectively as opposed to the state-of-the-art methods.
Intelligence and Adaptive Global Algorithm Detection of Crowd Behavior

The recognition and prediction of people's activities from videos are major concerns in the field of computer vision. The main objective of this article is to propose an adaptive global algorithm that analyzes human behavior from video. This problem is also called video content analysis or VCA. This analysis is performed in outdoor or indoor environments. The video scene can be depending on the number of people present, is characterized by the presence of only one person at a time in the video. We are interested in scenes containing a large number of people. This is called crowd scenes where we will address the problems of motion pattern extraction in crowd event detection. To achieve our goals, we propose an approach based on scheme analysis of a new adaptive architecture and hybrid technique detection movement. The first stage consists of acquiring the image from camera recordings. After several successive stages are applied, the active detection of movement by a hybrid technique, until classification by fuzzy logic is preformed, which is the last phase intervening in the process of detection of anomalies based on the increase in the speed of the reaction of safety services in order to carry out a precise analysis and detect events in real time. In order to provide the users with concrete results on the analysis of human behavior, result experimentation on datasets have validated our approaches, with very satisfying results compared to the other state-of-the-art approaches.
An Interactive Demonstration of Collaborative VR for Laparoscopic Liver Surgery Training

We introduce a collaborative virtual reality (VR) system for planning and simulation in laparoscopic liver surgery training. Patient image data is used for surgical model visualization and simulation. We developed two modes for training in laparoscopic procedures: exploration and surgery mode. Surgical joysticks are used in surgery mode to provide training for psychomotor skills and cooperation between a camera assistant and an experienced surgeon. Continuous feedback from our clinical partner comprised an important part of the development. Our evaluation showed that surgeons were positive about the usability and usefulness of the developed system. For further details, please refer to our full article and additional materials.
Node-Failure-Resistant Preconditioned Conjugate Gradient Method without Replacement Nodes

As HPC systems grow in scale to meet increased computational demands, the incidence of faults in a given window of time is expected to grow. This issue is addressed by the scientific community with research on solutions in every computational layer. In this paper, we explore strategies for fault tolerance at the algorithmic level. We propose a node-failure-tolerant preconditioned conjugate gradient method, which is able to efficiently recover from node failures without the use of extra spare nodes, i. e., without any overhead in terms of available hardware. For purposes of load balancing, we redistribute the surviving and reconstructed solver data. The objective is to reconstruct the system either as it was before the node failure, or an equivalent, permuted version, and then continue the execution of the solver only on the surviving nodes. In our experimental evaluations, the recovery stage of the solver typically takes around 10% or less of the solver runtime, including the time to retrieve the problem-defining static data from the hard disk, and, when using a suitable preconditioner, an average solver runtime overhead of 3.5% over that of a resilient solver that uses a replacement node. We investigate the influence of the preconditioner on a trade-off between load-balancing and communication cost in the recovery phase. The obtained solutions are correct, and our method is thus a feasible way to recover from a node failure and continue the execution of the solver only on the surviving nodes.
Multiple Universum Empirical Kernel Learning

This paper proposes a novel framework called Multiple Universum Empirical Kernel Learning (MUEKL) that combines the Universum learning with Multiple Empirical Kernel Learning (MEKL) for the first time to inherit the advantages of both techniques. The proposed MUEKL not only obtained supplementary information of multiple feature spaces through MEKL, but also obtained priori information of samples by Universum learning. MUEKL incorporates a novel method, Imbalanced Modified Universum (IMU), to generate more efficient Universum samples by introducing the imbalanced ratio of data. MUEKL develops the basic multiple kernel learning framework by introducing a regularization of Universum data. The function of the introduced regularization is to adjust the classifier boundary closer to the Universum data to alleviate the influence of the imbalanced data. Moreover, MUEKL performs excellent generalization for both the imbalanced and balanced problems. Extensive experiments verify the effectiveness of the MUEKL and IMU.
Multi-class Document Classification Using Improved Word Embeddings

In this paper, we conducted an experiment to build a classification model that combines different techniques in most of the Natural Language Processing Tasks. We used the word embedding method to transform every word in the dataset and to obtain the custom-built word embedding vectors. This is in contrast to the approaches in the previous literature that implement word embedding using the pre-trained word embedding vectors. We enriched the custom-built word embedding vectors by incorporating Part-of-Speech (POS) tag vectors to provide additional semantic information about the word to be used in training our proposed classification model. The proposed model was built using the neural network approach, which is considered to be more efficient and reliable in solving real problems for document classification tasks. We fine-tuned the parameters during the training of our neural network classification model with our aim to increase the performance in terms of classification accuracy. The experimental result demonstrates that our model performs remarkably well and increase the percentage accuracy up to 1.7% compared to the accuracy results obtained by the previous baseline word embedding methods using the same dataset. It was also observed that our model outperforms some other traditional classification models implemented using different techniques and machine learning algorithms.
A Novel Movie Recommendation System Based on Deep Reinforcement Learning with Prioritized Experience Replay

A recommendation system plays an important role in information overload case by recommending personalized services to improve user experience. In this paper, a novel movie recommendation system based on deep reinforcement learning (DRL) framework is proposed. In proposed system model, the state information is preprocessed to overcome the problems of data sparsity and cold start. Specially, user's interest change is captured using cross entropy and used to prioritize experience replay in a replay memory. The experiments verify that the proposed model can speed up the network update and improve recommendation accuracy.
Learning passive–aggressive correlation filter for long-term and short-term visual tracking

Correlation filter (CF) has received increasing attention in online visual object tracking. By accelerating the correlation in the frequency domain, CF trackers have achieved superior performance. However, existing CF trackers have a common shortcoming in that tracking models are prone to drift due to error accumulation. To address this problem, we propose a two-stage cascaded framework for accurate object modeling. In the first stage, we propose a passive–aggressive correlation filter (PACF) tracker to reduce error accumulation. In the subsequent stage, an online refinement algorithm is used to calibrate the tracking model by exploiting both long-term and short-term cues. In order to achieve high efficiency, our scheme reuses the PACF tracking response in the following stage. Extensive experiments were conducted on both long-term and short-term visual tracking benchmarks. The experimental results demonstrate that our tracker outperforms the state-of-the-art online tracking schemes in both long-term and short-term settings. Finally, we present a comprehensive analysis to validate the efficacy of our proposed method.
“PhysIt”—A Diagnosis and Troubleshooting Tool for Physiotherapists in Training

Many physiotherapy treatments begin with a diagnosis process. The patient describes symptoms, upon which the physiotherapist decides which tests to perform until a final diagnosis is reached. The relationships between the anatomical components are too complex to keep in mind and the possible actions are abundant. A trainee physiotherapist with little experience naively applies multiple tests to reach the root cause of the symptoms, which is a highly inefficient process. This work proposes to assist students in this challenge by presenting three main contributions: (1) A compilation of the neuromuscular system as components of a system in a Model-Based Diagnosis problem; (2) The PhysIt is an AI-based tool that enables an interactive visualization and diagnosis to assist trainee physiotherapists; and (3) An empirical evaluation that comprehends performance analysis and a user study. The performance analysis is based on evaluation of simulated cases and common scenarios taken from anatomy exams. The user study evaluates the efficacy of the system to assist students in the beginning of the clinical studies. The results show that our system significantly decreases the number of candidate diagnoses, without discarding the correct diagnosis, and that students in their clinical studies find PhysIt helpful in the diagnosis process.
Natural Language Processing for Information and Project Management

The purpose of the paper is to investigate the state of the art of textual translation theories, methods, and tools into formal and numerical requirements to support information modelling and project management process. Natural Language Processing helps translate text requirements into numerical terms that are necessary for the application and success of information modelling and management in a data-driven process.
ActiMon: Unified JOP and ROP Detection With Active Function Lists on an SoC FPGA

Field programmable gate arrays (FPGAs) have been increasingly mounted on commodity systems.As a matter of fact, such an emerging adoption of FPGAs in the commodity systems is attributed to their versatility came from the programmable property.Accordingly many industrial and academic attempts have been performed to exploit FPGAs in a variety of applications.In this paper, we note that FPGAs also can be used to protect the host CPU from a nasty security threat, called code reuse attacks (CRAs).Code reuse attack (CRA) is a powerful technique that allows attackers to execute arbitrary code.Control-flow integrity (CFI) has been popularly employed to mitigate CRAs.CFI entails CRA monitoring that checks if a program runs as directed by its control-flow graph.However, as monitoring naturally incurs non-negligible runtime overhead to the host CPU, many studies proposed hardware techniques to lessen the monitoring overhead.To facilitate the immediate deployment of a hardware-based solution, we propose a CRA monitor, called ActiMon, that can be implemented on an SoC FPGA where the host CPU and FPGA are manufactured together in a single platform.However, implementing the CRA monitor operating on FPGA arouses a new challenge that has never been addressed in the previous solutions: the operating clock of FPGA is many times slower than the CPU.By overcoming this speed difference, we ultimately purpose to evince the feasibility of FPGA as a computing device in the field of CRA defense.For this purpose, we have developed a highly efficient algorithm designed to run on FPGA whose goal is to monitor the existence of CRAs on the host CPU residing in the same SoC FPGA platform.Empirical results show that ActiMon runs on our target SoC FPGA platform efficiently enough to catch up to the speed of host code execution and promptly detects two important types of CRAs, JOP (Jump-Oriented Programming) and ROP (Return-Oriented Programming), as soon as they occurred in the host system.We assert that such results are encouraging thanks to our unified, lightweight ROP/JOP detection mechanism based on a list of active functions, and also to additional optimizations to leverage the inherent capabilities of FPGA for parallel computation. INDEX TERMS Code reuse attacks (CRAs),
Edu-Coin: A Proof of Stake implementation of a decentralized skill validation application

This paper introduces a use case for blockchain in the field of education. The concept of a social networking DApp (Decentralised App) Edu-Coin that uses the concept of giving the control of their data back to the users, reward them for sharing their skills and using the platform. This idea uses the Tendermint core for blockchain, a framework in which individuals can make peer-to-peer transactions without needing to trust a third party, a blockchain that is based on the Proof of Stake. In Edu-Coin, working professionals can ensure that their skills are validated by an unbiased majority, called validators. It also assesses the Emotional Quotient of an individual. Validators whose responses get accepted to the blockchain are able to earn Edu-Coin, which can be utilized for making different purchases on the site. The platform makes use of one tradable token (Edu-Coin) and one internal accounting token (ERP) that serves as an evaluation of the users on the respective skill sets. This rating can be useful in the evaluation process of educational institutes as well as for professional grading of employees and job applicants.