Reviewerly/exHarmony huggingface dataset:
Perceptions and use of cloud in academic activities: a study of a public university in Bangladesh

Purpose The purpose of this paper is to explore the initial understanding of what perceptions have the faculty members of Dhaka University and how they use cloud for academic activities. Design/methodology/approach A semi-structured questionnaire was used to collect responses from the faculty members of Dhaka University. An online questionnaire was sent out to the faculty members of Dhaka University via e-mail. The questionnaire was both open and close-ended. Overall, 125 respondents participated in this survey and convenience sampling method was used. Mann–Whitney and chi-square test were carried out to see the influence of demographic characteristics on cloud use for teaching and learning activities. Findings The results showed that faculty members are generally aware of cloud computing, and they are using some of the cloud applications for teaching and learning purposes. They use cloud for research, collaboration, faculty-students engagement, student supporting activities and sharing course materials. They identified limited wireless access or internet facilities, lack of ICT infrastructure and lack of cloud training and knowledge as major constraints. However, these constraints are mainly related to the poor IT infrastructure and limited access to internet, which may also lead to other constraints. Originality/value This is the first time an attempt has been made to explore the perception and use of cloud among the faculty members in a public university, Bangladesh. The findings of this study may shed light to other align research areas of cloud computing in Bangladesh.
Interaction with Formal Mathematical Documents in Isabelle/PIDE

Isabelle/PIDE has emerged over more than 10 years as the standard Prover IDE for interactive theorem proving in Isabelle. The well-established Archive of Formal Proofs (AFP) testifies the success of such applications of formalized mathematics in Isabelle/HOL. More recently, the scope of PIDE has widened towards languages that are not connected to logic and proof in Isabelle, but taken from a broader repertoire of mathematics on the computer. The present paper provides a general overview of the PIDE project and its underlying document model, with built-in parallel evaluation and asynchronous interaction. There is also some discussion of original aims and approaches, successes and failures, later changes to the plan, and ideas for the future.
Universality of Computational Lower Bounds for Submatrix Detection

In the general submatrix detection problem, the task is to detect the presence of a small $k \times k$ submatrix with entries sampled from a distribution $\mathcal{P}$ in an $n \times n$ matrix of samples from $\mathcal{Q}$. This formulation includes a number of well-studied problems, such as biclustering when $\mathcal{P}$ and $\mathcal{Q}$ are Gaussians and the planted dense subgraph formulation of community detection when the submatrix is a principal minor and $\mathcal{P}$ and $\mathcal{Q}$ are Bernoulli random variables. These problems all seem to exhibit a universal phenomenon: there is a statistical-computational gap depending on $\mathcal{P}$ and $\mathcal{Q}$ between the minimum $k$ at which this task can be solved and the minimum $k$ at which it can be solved in polynomial time. Our main result is to tightly characterize this computational barrier as a tradeoff between $k$ and the KL divergences between $\mathcal{P}$ and $\mathcal{Q}$ through average-case reductions from the planted clique conjecture. These computational lower bounds hold given mild assumptions on $\mathcal{P}$ and $\mathcal{Q}$ arising naturally from classical binary hypothesis testing. Our results recover and generalize the planted clique lower bounds for Gaussian biclustering in Ma-Wu (2015) and Brennan et al. (2018) and for the sparse and general regimes of planted dense subgraph in Hajek et al. (2015) and Brennan et al. (2018). This yields the first universality principle for computational lower bounds obtained through average-case reductions.
Time evolution of angular momentum coherent state derived by virtue of entangled state representation and a new binomial theorem*

We study how can an angular momentum coherent state <?CDATA $| \tau \rangle $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">∣</mml:mo> <mml:mi>τ</mml:mi> <mml:mo stretchy="false">〉</mml:mo> </mml:math> keeps its form-invariant during time evolution governed by the Hamiltonian <?CDATA $H=f(t){J}_{+}+{f}^{* }(t){J}_{-}+g(t){J}_{z}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>H</mml:mi> <mml:mo>=</mml:mo> <mml:mi>f</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>t</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:msub> <mml:mrow> <mml:mi>J</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> </mml:mrow> </mml:msub> <mml:mo>+</mml:mo> <mml:msup> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>*</mml:mo> </mml:mrow> </mml:msup> <mml:mo stretchy="false">(</mml:mo> <mml:mi>t</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:msub> <mml:mrow> <mml:mi>J</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> </mml:mrow> </mml:msub> <mml:mo>+</mml:mo> <mml:mi>g</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>t</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:msub> <mml:mrow> <mml:mi>J</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>z</mml:mi> </mml:mrow> </mml:msub> </mml:math> . We discuss this topic in the context of boson realization of <?CDATA $| \tau \rangle $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">∣</mml:mo> <mml:mi>τ</mml:mi> <mml:mo stretchy="false">〉</mml:mo> </mml:math> . By employing the entangled state representation <?CDATA $| \zeta \rangle $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">∣</mml:mo> <mml:mi>ζ</mml:mi> <mml:mo stretchy="false">〉</mml:mo> </mml:math> and deriving a new binomial theorem involving two-subscript Hermite polynomials, we derive the wave function <?CDATA $\langle \zeta | \tau \rangle $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">〈</mml:mo> <mml:mi>ζ</mml:mi> <mml:mo stretchy="false">∣</mml:mo> <mml:mi>τ</mml:mi> <mml:mo stretchy="false">〉</mml:mo> </mml:math> , which turns out to be a single-subscript Hermite polynomial. Based on this result the maintenance of angular momentum coherent state during time evolution is examined, and the value of <?CDATA $\tau (t)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>τ</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>t</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> is totally determined by the parameters involved in the Hamiltonian.
Semantic nuclei segmentation with deep learning on breast pathology images

The methods of facilitating the workload of doctors are tried to be developed in the diagnosis of cancer. One of these procedures is the segmentation of cell nuclei in digital images obtained in the field of pathology. For the segmentation process, deep-learning can be performed with local small patches obtained from the digital image, and pixel-based systems can be developed by using semantic segmentation technique. In this study, histopathological images obtained from hematoxylin and eosin staining are used for biopsy samples taken for diagnosis of breast cancer. The studies were performed in Matlab environment by using SegNet and U-Net algorithms and the accuracy of semantic segmentation was evaluated comparatively.
Shared Body by Action Integration of Two Persons: Body Ownership, Sense of Agency and Task Performance

Humans have one own body. However, we can share a body with two different persons in a virtual environment. We have developed a shared body as an avatar that is controlled by two persons' actions. Movements of two subjects were continuously captured, and integrated into the avatar's motion with the ratios of 0:100,25:75, 50:50, 75:25, 100:0. They were not aware of integration ratios, and asked to reach cubes with the right hand. They felt body ownership and sense of agency to the shared avatar more when the responsible ratio was higher. The reaching path of the avatar's hand was shorter in the shared body condition (75:25) than the single body condition (100:0). These results suggest that we have some of body ownership and sense of agency to the shared body, and the task performance is improved by the shared body.
Reducing Seasickness in Onboard Marine VR Use through Visual Compensation of Vessel Motion

We developed a virtual reality interface for cleaning sonar point cloud data. Experimentally, users performed better when using this VR interface compared to a mouse-and-keyboard with a desktop monitor. However, hydrographers often clean data aboard moving vessels, which can create motion sickness. Users of VR experience motion sickness as well, in the form of simulator sickness. Combining the two is a worst-case scenario for motion sickness. Advice for avoiding seasickness includes focusing on the horizon or objects in the distance, to keep your frame of reference external. We explored moving the surroundings in a virtual environment to match vessel motion, to assess whether it provides similar visual cues that could prevent seasickness. An informal evaluation in a seasickness-inducing simulator was conducted, and subjective preliminary results hint at such compensation's potential for reducing motion sickness, enabling the use of immersive VR technologies aboard underway ships.
SPOILER: Speculative Load Hazards Boost Rowhammer and Cache Attacks

Modern microarchitectures incorporate optimization techniques such as speculative loads and store forwarding to improve the memory bottleneck. The processor executes the load speculatively before the stores, and forwards the data of a preceding store to the load if there is a potential dependency. This enhances performance since the load does not have to wait for preceding stores to complete. However, the dependency prediction relies on partial address information, which may lead to false dependencies and stall hazards. In this work, we are the first to show that the dependency resolution logic that serves the speculative load can be exploited to gain information about the physical page mappings. Microarchitectural side-channel attacks such as Rowhammer and cache attacks like Prime+Probe rely on the reverse engineering of the virtual-to-physical address mapping. We propose the SPOILER attack which exploits this leakage to speed up this reverse engineering by a factor of 256. Then, we show how this can improve the Prime+Probe attack by a 4096 factor speed up of the eviction set search, even from sandboxed environments like JavaScript. Finally, we improve the Rowhammer attack by showing how SPOILER helps to conduct DRAM row conflicts deterministically with up to 100% chance, and by demonstrating a double-sided Rowhammer attack with normal user's privilege. The later is due to the possibility of detecting contiguous memory pages using the SPOILER leakage.
gEYEded: Subtle and Challenging Gaze-Based Player Guidance in Exploration Games

This paper investigates the effects of gaze-based player guidance on the perceived game experience, performance, and challenge in a first-person exploration game. In contrast to existing research, the proposed approach takes the game context into account by providing players not only with guidance but also granting them an engaging game experience with a focus on exploration. This is achieved by incorporating gaze-sensitive areas that indicate the location of relevant game objects. A comparative study was carried out to validate our concept and to examine if a game supported with a gaze guidance feature triggers a more immersive game experience in comparison to a crosshair guidance version and a solution without any guidance support. In general, our study findings reveal a more positive impact of the gaze-based guidance approach on the experience and performance in comparison to the other two conditions. However, subjects had a similar impression concerning the game challenge in all conditions.
Exact Rate-Distortion in Autoencoders via Echo Noise

Compression is at the heart of effective representation learning. However, lossy compression is typically achieved through simple parametric models like Gaussian noise to preserve analytic tractability, and the limitations this imposes on learning are largely unexplored. Further, the Gaussian prior assumptions in models such as variational autoencoders (VAEs) provide only an upper bound on the compression rate in general. We introduce a new noise channel, Echo noise, that admits a simple, exact expression for mutual information for arbitrary input distributions. The noise is constructed in a data-driven fashion that does not require restrictive distributional assumptions. With its complex encoding mechanism and exact rate regularization, Echo leads to improved bounds on log-likelihood and dominates beta-VAEs across the achievable range of rate-distortion trade-offs. Further, we show that Echo noise can outperform flow-based methods without the need to train additional distributional transformations.
Medical Image Super-Resolution Based on the Generative Adversarial Network

In order to assist doctors to read medical pathological images with low resolution, this paper proposes a medical image super-resolution (SR) reconstruction method based on generative adversarial network (GAN). Considering that the pathological image has large non-organized regions, we design a medical pathological image preprocessing system to extract tissue area image patches. And, we improve discriminator with small batch relative discrimination to enhance the quality of reconstructed images by learning more prior information. We use Huber loss instead of the original MSE which can keep the network training stable. We find the feature similarity (FSIM) is suitable as an image quality evaluation way for medical image reconstruction research. And, the experimental results show the advantages of our method in the restoration of color and intercellular texture details.
The Future of Cybercrime Prevention Strategies: Human Factors and A Holistic Approach to Cyber Intelligence

New technology is rapidly emerging to fight increasing cybercrime threats, however, there is one important component of a cybercrime that technology cannot always impact and that is human behavior. Unfortunately, humans can be vulnerable and easily deceived making technological advances alone inadequate in the cybercrime fight. Instead, we must take a more holistic approach by using technology and better understanding the human factors that make cybercrime possible. In this issue of the International Journal of Cybersecurity Intelligence and Cybercrime, three studies contribute to our knowledge of human factors and emerging cybercrime technology so that more effective comprehensive cybercrime prevention strategies can be developed.
Comparison of deep learning techniques on content based image retrieval

Content based Image Retrieval is mostly utilized to extract the pictures from large database. CBIR, which is additionally known as “Query by image” is a technology allowing organizing the computerized Images by their visible attributes. Or, in other words, CBIR is a method for retrieving pictures dependent not on annotations or keywords, but dependent on the feature taken out straightly from the pictures database. CBIR systems are dependent on the utilizations of computer vision methods to the image retrieval issue in huge databases. CBIR is technology of recovering the utmost visually identical pictures to a specified query picture from a cluster or database of pictures. It is useful in a lot of areas like Photography to search images from the database, medical diagnosis etc. Physically annotating the pictures by inputting the metadata or keywords in a huge database can be laborious and might not capture the keyword anticipated to define that picture. CBIR supports in recovering identical pictures from an database of pictures deprived of pictures annotation. In this paper, we are compare the Deep Neural Networks and Neuro-Fuzzy Classifier, both have different outcomes and different results to predict the image. The comparison of our proposed methods Neuro-fuzzy classification and deep neural network shows that the improvement in accuracy. The accuracy values 71.6% and 73.4% for DNN and Neuro-Fuzzy Classifier[Formula: see text] method. The visual and qualitative results are presented for validation of the proposed method.
Multi-objective strategy to optimize dithering technique for high-quality three-dimensional shape measurement*

Dithering optimization techniques can be divided into the phase-optimized technique and the intensity-optimized technique. The problem with the former is the poor sensitivity to various defocusing amounts, and the problem with the latter is that it cannot enhance phase quality directly nor efficiently. In this paper, we present a multi-objective optimization framework for three-dimensional (3D) measurement by utilizing binary defocusing technique. Moreover, a binary patch optimization technique is used to solve the time-consuming issue of genetic algorithm. It is demonstrated that the presented technique consistently obtains significant phase performance improvement under various defocusing amounts.
End-to-End Neural Context Reconstruction in Chinese Dialogue

We tackle the problem of context reconstruction in Chinese dialogue, where the task is to replace pronouns, zero pronouns, and other referring expressions with their referent nouns so that sentences can be processed in isolation without context.Following a standard decomposition of the context reconstruction task into referring expression detection and coreference resolution, we propose a novel end-toend architecture for separately and jointly accomplishing this task.Key features of this model include POS and position encoding using CNNs and a novel pronoun masking mechanism.One perennial problem in building such models is the paucity of training data, which we address by augmenting previouslyproposed methods to generate a large amount of realistic training data.The combination of more data and better models yields accuracy higher than the state-of-the-art method in coreference resolution and end-to-end context reconstruction.
Deep Architectures for Joint Clustering and Visualization with Self-organizing Maps

Recent research has demonstrated how deep neural networks are able to learn representations to improve data clustering. By considering representation learning and clustering as a joint task, models learn clustering-friendly spaces and achieve superior performance, compared with standard two-stage approaches where dimensionality reduction and clustering are performed separately. We extend this idea to topology-preserving clustering models, known as self-organizing maps (SOM). First, we present the Deep Embedded Self-Organizing Map (DESOM), a model composed of a fully-connected autoencoder and a custom SOM layer, where the SOM code vectors are learnt jointly with the autoencoder weights. Then, we show that this generic architecture can be extended to image and sequence data by using convolutional and recurrent architectures, and present variants of these models. First results demonstrate advantages of the DESOM architecture in terms of clustering performance, visualization and training time.
Human Activity Recognition using Visual Object Detection

Visual Human Activity Recognition (HAR), by means of an object detection algorithm, can be used to localize and monitor the states of people with little to no obstruction. The purpose of this paper is to discuss a way to train a model that has the ability to localize and capture the states of underground miners using a Single Shot Detector (SSD) model, trained specifically to make a distinction between an injured and a non- injured miner (lying down vs standing up). Tensorflow is used for the abstraction layer of implementing the machine learning algorithm, and although it uses Python to deal with nodes and tensors, the actual algorithms run on C++ libraries, providing a good balance between performance and speed of development. The paper further discusses evaluation methods for determining the accuracy of the machine-learning progress. For future work, data fusion is introduced in order to improve the accuracy of the detected activity/state of people in a mining environment.
Audit-Based Access Control with a Distributed Ledger: Applications to Healthcare Organizations

We propose an audit-based architecture that leverages the Hyperledger Fabric distributed ledger as a means to increase accountability and decentralize the authorization decision process of Attribute-Based Access Control policies by using smart contracts. Our goal is to decrease the trust in administrators and users with privileged accounts, and make the a posteriori verification of access events more reliable. We implement our approach to the use case of Electronic Health Record access control. Preliminary experiments show the viability of the proposed approach.
Incremental rare pattern based approach for identifying outliers in medical data

Abstract Outlier detection is an imperative field of data mining that has several applications in the field of medical research. Mining outliers based on the notion of rare patterns can be a promising solution for medical diagnosis as it attempts to identify the unconventional and abnormal risk patterns present in medical data. A crucial issue in medical data analysis is the continuous growth of medical databases due to the addition of new records. Existing outlier detection techniques are capable of handling only static data and thus re-execute from scratch to identify the outliers from incremental medical data. This paper introduces an efficient rare pattern based outlier detection (RPOD) method that identifies outliers by mining rare patterns from incremental data. To avoid multiple database scans and expensive candidate generation steps performed by existent rare pattern mining techniques and facilitate incremental mining, a single pass prefix tree-based rare pattern mining technique is proposed. The proposed rare pattern mining technique is a modification of the well-known FP-Growth frequent pattern mining algorithm. Furthermore, to identify the outliers based on the set of generated rare patterns, an outlier detection technique is also presented. The significance of proposed RPOD approach is demonstrated using several well-known medical datasets. Comparative performance evaluation substantiates the predominance of RPOD approach over existing outlier mining methods.
An Automated CNN-based 3D Anatomical Landmark Detection Method to Facilitate Surface-Based 3D Facial Shape Analysis

Maternal alcohol consumption during pregnancy can lead to a wide range of physical and neurodevelopmental problems, collectively known as fetal alcohol spectrum disorders (FASD). In many cases, diagnosis is heavily reliant on the recognition of a set of characteristic facial features, which can be subtle and difficult to objectively identify. To provide an automated and objective way to quantify these features, this paper proposes to take advantage of high-resolution 3D facial scans collected from a high-risk population. We present a method to automatically localize anatomical landmarks on each face, and align them to a standard space. Subsequent surface-based morphology analysis or anatomical measurements demands that such a method is both accurate and robust. The CNN-based model uses a novel differentiable spatial to numerical transform (DSNT) layer that could transform spatial activation to numerical values directly, which enables end-to-end training. Experiments reveal that the inserted layer helps to boost the performance and achieves sub-pixel level accuracy.