Reviewerly/exHarmony huggingface dataset:
Towards Accurate and Robust Multi-Modal Medical Image Registration Using Contrastive Metric Learning

Multi-modal medical image registration takes an essential role in image-based clinical diagnosis and surgical planning. It is not trivial due to appearance variations across different modalities. Rigidly aligning two images is used to register rigid body structure, and it is also usually the first step for deformable registration with a large discrepancy. In the field of computer vision, one well-established method for image alignment is to find corresponding points from two images, and image alignment is based on identified corresponding points. Our method lies in this category. Feature representation is crucial in finding corresponding points. However, conventional feature representation like SIFT does not take multi-modal information into account, and thus, it fails. In this paper, we propose a Convolution Neural Network Feature-based Registration (CNNFR) method for aligning the multi-modal medical image. The important component in this method is learning keypoint descriptors using contrastive metric learning, which minimizes the difference between two feature representations from two corresponding points and maximizes difference of two feature representation from two distant points. Also, a transfer learning-based CNNRF (TrCNNRF) is proposed to improve the generalization learning performance when the training data are insufficient. Experimental results demonstrate that the proposed methods can achieve superior performance regarding both accuracy and robustness, which can be used to rigidly register multi-modal images and provide an initial estimation for non-rigid registration in clinical practices.
Cyclone

Micro-architecture units like caches are notorious for leaking secrets across security domains. An attacker program can contend for on-chip state or bandwidth and can even use speculative execution in processors to drive this contention; and protecting against all contention-driven attacks is exceptionally challenging. Prior works can mitigate contention channels through caches by partitioning the larger, lower-level caches or by looking for anomalous performance or contention behavior. Neither scales to large number of fine-grained domains as required by browsers and web-services that place many domains within the same address space.
Facial expression recognition based on Gabor wavelet transform and 2-channel CNN

Facial expression recognition is one of the hotspots in the fields of computer vision and deep learning. It has very important applications in the domains of learning service recommendation, human–computer interaction and medical industry. Aiming at the problem that the traditional expression recognition method is not accurate, this paper proposes a method combining Gabor wavelet transform and convolutional neural network. Firstly, face positioning, cropping, histogram equalization and other preprocessing are performed on the expression image. Then we extract key frames of expression sequences. After that the Gabor wavelet transform is performed on the expression image to obtain magnitude and phase characteristics. Finally, we design a 2-channel CNN for training and classification. The experiment achieves an accuracy of 96.81% on the CK+ database and it has a certain improvement compared with the Gabor wavelet transform and the traditional CNN alone.
Tripartite Entanglement Measures of Generalized GHZ State in Uniform Acceleration<sup>*</sup>

Using the single-mode approximation, we study entanglement measures including two independent quantities; i.e., negativity and von Neumann entropy for a tripartite generalized Greenberger–Horne–Zeilinger (GHZ) state in noninertial frames. Based on the calculated negativity, we study the whole entanglement measures named as the algebraic average π 3 -tangle and geometric average Π 3 -tangle. We find that the difference between them is very small or disappears with the increase of the number of accelerated qubits. The entanglement properties are discussed from one accelerated observer and others remaining stationary to all three accelerated observers. The results show that there will always exist entanglement, even if acceleration r arrives to infinity. The degree of entanglement for all 1–1 tangles are always equal to zero, but 1–2 tangles always decrease with the acceleration parameter r. We notice that the von Neumann entropy increases with the number of the accelerated observers and S κ I ζ I ( κ, ζ ∈ (A, B, C)) first increases and then decreases with the acceleration parameter r. This implies that the subsystem ρ κ I ζ I is first more disorder and then the disorder will be reduced as the acceleration parameter r increases. Moreover, it is found that the von Neumann entropies S ABCI , S ABICI and S AIBICI always decrease with the controllable angle θ, while the entropies of the bipartite subsystems S 2−2 non (two accelerated qubits), S 2-1 non (one accelerated qubit) and S 2-0 non (without accelerated qubit) first increase with the angle θ and then decrease with it.
FACT and FAIR with Big Data allows objectivity in science: The view of crystallography

A publication is an important narrative of the work done and interpretations made by researchers securing a scientific discovery. As The Royal Society neatly states though, "Nullius in verba" ("Take nobody's word for it"), whereby the role of the underpinning data is paramount. Therefore, the objectivity that preserving that data within the article provides is due to readers being able to check the calculation decisions of the authors. But how to achieve full data archiving? This is the raw data archiving challenge, in size and need for correct metadata. Processed diffraction data and final derived molecular coordinates archiving in crystallography have achieved an exemplary state of the art relative to most fields. One can credit IUCr with developing exemplary peer review procedures, of narrative, underpinning structure factors and coordinate data and validation report, through its checkcif development and submission system introduced for Acta Cryst. C and subsequently developed for its other chemistry journals. The crystallographic databases likewise have achieved amazing success and sustainability these last 50 years or so. The wider science data scene is celebrating the FAIR data accord, namely, that data be Findable, Accessible, Interoperable, and Reusable [Wilkinson et al., "Comment: The FAIR guiding principles for scientific data management and stewardship," Sci. Data 3, 160018 (2016)]. Some social scientists also emphasize more than FAIR being needed, the data should be "FACT," which is an acronym meaning Fair, Accurate, Confidential, and Transparent [van der Aalst et al., "Responsible data science," Bus Inf. Syst. Eng. 59(5), 311-313 (2017)], this being the issue of ensuring reproducibility not just reusability. (Confidentiality of data not likely being relevant to our data obviously.) Acta Cryst. B, C, E, and IUCrData are the closest I know to being both FACT and FAIR where I repeat for due emphasis: the narrative, the automatic "general" validation checks, and the underpinning data are checked thoroughly by subject specialists (i.e., the specialist referees). IUCr Journals are also the best that I know of for encouraging and then expediting the citation of the DOI for a raw diffraction dataset in a publication; examples can be found in IUCrJ, Acta Cryst D, and Acta Cryst F. The wish for a checkcif for raw diffraction data has been championed by the IUCr Diffraction Data Deposition Working Group and its successor, the IUCr Committee on Data.
Improving User Experience of Eye Tracking-Based Interaction

Eye tracking systems have greatly improved in recent years, being a viable and affordable option as digital communication channel, especially for people lacking fine motor skills. Using eye tracking as an input method is challenging due to accuracy and ambiguity issues, and therefore research in eye gaze interaction is mainly focused on better pointing and typing methods. However, these methods eventually need to be assimilated to enable users to control application interfaces. A common approach to employ eye tracking for controlling application interfaces is to emulate mouse and keyboard functionality. We argue that the emulation approach incurs unnecessary interaction and visual overhead for users, aggravating the entire experience of gaze-based computer access. We discuss how the knowledge about the interface semantics can help reducing the interaction and visual overhead to improve the user experience. Thus, we propose the efficient introspection of interfaces to retrieve the interface semantics and adapt the interaction with eye gaze. We have developed a Web browser, GazeTheWeb, that introspects Web page interfaces and adapts both the browser interface and the interaction elements on Web pages for gaze input. In a summative lab study with 20 participants, GazeTheWeb allowed the participants to accomplish information search and browsing tasks significantly faster than an emulation approach. Additional feasibility tests of GazeTheWeb in lab and home environment showcase its effectiveness in accomplishing daily Web browsing activities and adapting large variety of modern Web pages to suffice the interaction for people with motor impairment.
Distilling the Knowledge of BERT for Text Generation

Large-scale pre-trained language model, such as BERT, has recently achievedgreat success in a wide range of language understanding tasks. However, itremains an open question how to utilize BERT for text generation tasks. In thispaper, we present a novel approach to addressing this challenge in a genericsequence-to-sequence (Seq2Seq) setting. We first propose a new task,Conditional Masked Language Modeling (C-MLM), to enable fine-tuning of BERT ontarget text-generation dataset. The fine-tuned BERT (i.e., teacher) is thenexploited as extra supervision to improve conventional Seq2Seq models (i.e.,student) for text generation. By leveraging BERT's idiosyncratic bidirectionalnature, distilling the knowledge learned from BERT can encourageauto-regressive Seq2Seq models to plan ahead, imposing global sequence-levelsupervision for coherent text generation. Experiments show that the proposedapproach significantly outperforms strong baselines of Transformer on multipletext generation tasks, including machine translation (MT) and textsummarization. Our proposed model also achieves new state-of-the-art results onthe IWSLT German-English and English-Vietnamese MT datasets.
Designing for Awareness in Interactions with Shared Systems

Most systems that we use in everyday life are shared—because multiple people can interact or because an interaction by one person can affect other people. However, everyday Internet of Things systems are often designed for individual use. Prior research on collaboration technologies (Computer Supported Collaborative Work) has shown that to coordinate system sharing people require awareness of the social context, which interfaces can support by making salient information visible. Although literature exists on how to design for awareness, this can be fragmented and difficult to relate to other application domains. To introduce a broader audience of interaction designers to awareness, we aim to make the available design knowledge more generalizable and operational. With this aim, we construct the Designing for Awareness in Shared Systems framework that gives a structured and comprehensive overview of design considerations for awareness. The framework can stimulate reflection and inform decision-making when designing interactions with shared systems.
Gerontotechnology for fall prevention of the elderly with Parkinson

ABSTRACT Objective: to develop the gerontological nursing care process among the elderly with Parkinson’s disease, aiming at the promotion of health through the creation of gerontotechnologies for fall prevention. Method: Convergent Care Research was used as a methodological route. Data were collected from February to October 2017, with the participation of nine elderly people with Parkinson’s disease. An educational booklet, a memory game called “não cai istepô”, a memory game called “caiu de maduro” were developed through clinical evaluation through scales, recorded semi-structured interview and workshops. Results: the application of gerontotechnology resulted from the elderly in self-care, empowerment and knowledge through play, revealing interest in behavior change, independence and learning, as well as serving as a facilitator of care. Conclusion: the gerontotechnologies presented as a playful and innovative instrument for the nursing gerontological care process.
SoK: A Systematic Study of Anonymity in Cryptocurrencies

Blockchain and cryptocurrencies have been widely deployed and used in our daily life. Although there are numerous works in the literature surveying technical challenges and security issues in blockchains, very few works focused on the anonymity guarantees provided in cryptocurrencies. In this work, we conduct a systematic survey on anonymity in cryptocurrencies with a clear categorization for the different tiers of anonymity offered in the various cryptocurrencies as well as their known weaknesses and vulnerabilities. We also study the techniques that have been used to achieve each tier of anonymity. Finally, we asses the current techniques, and present a forecast for the technological trends in this field.
On Bonus Based Exploration Methods In The Arcade Learning Environment

Research on exploration in reinforcement learning, as applied to Atari 2600 game-playing, has emphasized tackling difficult exploration problems such as Montezuma's Revenge (Bellemare et al., 2016). Recently, bonus-based exploration methods, which explore by augmenting the environment reward, have reached above-human average performance on such domains. In this paper we reassess popular bonus-based exploration methods within a common evaluation framework. We combine Rainbow (Hessel et al., 2018) with different exploration bonuses and evaluate its performance on Montezuma's Revenge, Bellemare et al.'s set of hard of exploration games with sparse rewards, and the whole Atari 2600 suite. We find that while exploration bonuses lead to higher score on Montezuma's Revenge they do not provide meaningful gains over the simpler epsilon-greedy scheme. In fact, we find that methods that perform best on that game often underperform epsilon-greedy on easy exploration Atari 2600 games. We find that our conclusions remain valid even when hyperparameters are tuned for these easy-exploration games. Finally, we find that none of the methods surveyed benefit from additional training samples (1 billion frames, versus Rainbow's 200 million) on Bellemare et al.'s hard exploration games. Our results suggest that recent gains in Montezuma's Revenge may be better attributed to architecture change, rather than better exploration schemes; and that the real pace of progress in exploration research for Atari 2600 games may have been obfuscated by good results on a single domain.
Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation

Although there are many existing research works about the salient object detection (SOD) in RGB images, there are still many complex situations that regular RGB images cannot provide enough cues for the accurate SOD, such as the shadow effect, similar appearance between background and foreground, strong or insufficient illumination, etc. Because of the success of near-infrared spectrum in many computer vision tasks, we explore the multi-spectral SOD in the synchronized RGB images and near-infrared (NIR) images for the both simple and complex situations. We assume that the RGB SOD in the existing RGB image datasets could provide references for the multi-spectral SOD problem. In this paper, we first collect and will publicize a large multi-spectral dataset including 780 synchronized RGB and NIR image pairs for the multi-spectral SOD problem in the simple and complex situations. We model this research problem as an adversarial domain adaptation from the existing RGB image dataset (source domain) to the collected multi-spectral dataset (target domain). Experimental results show the effectiveness and accuracy of the proposed adversarial domain adaptation for the multi-spectral SOD.
Image Enhancement Based Medical Image Analysis

Image enhancement is a vital step of medical image analysis and image recognition. X-ray and ultrasound imaging are the most preferred medical imaging technologies which are important for diagnosis of disease. But the edges and borders on image are not as clear as expected due to interference and low intensity in images. This paper presents an images enhancement techniques, specially in the case of medical images. Using image enhancement, it is possible to get the details which are kept hidden as well as to improve the image contrast. In the case of analyzing image, the commencing part is that the edge of an image. Successful results of image analysis depend on edge detection & enhancement. In this research we have developed a method of enhancement by incorporating Laplacian, Sobel operator, addition operation, filter, product operation and power law transformation techniques to enhance medical images. This developed method is tested on low contrast medical images and by observing the results it can be said that this applied methods perform well for enhancing medical images.
Relative inconsistency measures

The literature on inconsistency measures has ignored a distinction, that is, differentiating absolute measures and relative measures. An absolute measure gives the total amount of inconsistency in the knowledge base but a relative measure computes, by some criteria, the proportion of the base that is inconsistent. To compare the inconsistency measures, researchers have proposed postulates for such measures. We split these postulates into three groups: ones (including two new postulates) that relative measures should satisfy, ones inappropriate for relative measures, and ones that relative measures may satisfy. We obtain some new results upon the relationships between these groups of postulates. On these grounds, we introduce a formal definition for relative inconsistency measures. We consider some relative measures previously proposed and define several new ones that serve as examples. We show that all of these measures satisfy the new formal definition.
Using a Wavelet-Based and Fine-Tuned Convolutional Neural Network for Classification of Breast Density in Mammographic Images

Classification of breast density is significantly important during the process of breast diagnosis.The purpose of this study was to develop a useful computerized tool to help radiologists determine the patient's breast density category on the mammogram.In this article, we presented a model for automatically classifying breast densities by employing a wavelet transform-based and fine-tuned convolutional neural network (CNN).We modified a pre-trained AlexNet model by removing the last two fully connected (FC) layers and appending two newly created layers to the remaining structure.Unlike the common CNN-based methods that use original or pre-processed images as inputs, we adopted the use of redundant wavelet coefficients at level 1 as inputs to the CNN model.Our study mainly focused on discriminating between scattered density and heterogeneously dense which are the two most difficult density categories to differentiate for radiologists.The proposed system achieved 88.3% overall accuracy.In order to demonstrate the effectiveness and usefulness of the proposed method, the results obtained from a conventional fine-tuning CNN model was compared with that from the proposed method.The results demonstrate that the proposed technique is very promising to help radiologists and serve as a second eye for them to classify breast density categories in breast cancer screening.
Research on Clustering Method Based on Weighted Distance Density and K-Means

In this paper, the effect of the initial clustering center selection on the performance of the K-means algorithm is studied, and the performance of the algorithm is enhanced through better initialization techniques. In the K-means clustering process, when calculating the density of a data set by using a weighted distance density calculation method, significant improvement in the defects of poor clustering results caused by the local optimum and large intra-cluster variance in the traditional K-means clustering algorithm has been found. Experimental results show that by using the improved method proposed in this paper, the intra-cluster variance of clustering results is reduced by 15.5% compared with the traditional method, which makes great improvement in the performance of the algorithm.
Information system security policy noncompliance: the role of situation-specific ethical orientation

Purpose This study examines how neutralization strategies affect the efficacy of information system security policies. This paper proposes that neutralization strategies used to rationalize security policy noncompliance range across ethical orientations, extending from those helping the greatest number of people (ethics of care) to those damaging the fewest (ethics of justice). The results show how noncompliance differs between genders based on those ethical orientations. Design/methodology/approach A survey was used to measure information system security policy noncompliance intentions across six different hypothetical scenarios involving neutralization techniques used to justify noncompliance. Data was gathered from students at a mid-western, comprehensive university in the United States. Findings The empirical analysis suggests that gender does play a role in information system security policy noncompliance. However, its significance is dependent upon the underlying neutralization method used to justify noncompliance. The role of reward and punishment is contingent on the situation-specific ethical orientation (SSEO) which in turn is a combination of internal ethical positioning based on one's gender and external ethical reasoning based on neutralization technique. Originality/value This study extends ethical decision-making theory by examining how the use of punishments and rewards might be more effective in security policy compliance based upon gender. Importantly, the study emphasizes the interplay between ethics, gender and neutralization techniques, as different ethical perspectives appeal differently based on gender.
VoIP network covert channels to enhance privacy and information sharing

Information hiding is increasingly used to implement covert channels, to exfiltrate data or to perform attacks in a stealthy manner. Another important usage deals with privacy, for instance, to bypass limitations imposed by a regime, to prevent censorship or to share information in sensitive scenarios such as those dealing with cyber defense. In this perspective, the paper investigates how VoIP communications can be used as a methodology to enhance privacy. Specifically, we propose to hide traffic into VoIP conversations in order to prevent the disclosure, exposure and revelation to an attacker or blocking the ongoing exchange of information. To this aim, we exploit the voice activity detection feature available in many client interfaces to produce fake silence packets, which can be used as the carrier where to hide data. Results indicate that the proposed approach can be suitable to enforce the privacy in real use cases, especially for file transfers. As interactive services (e.g., web browsing) may experience too many delays due to the limited bandwidth, some form of optimization or content scaling may be advisable for such scenarios.