Reviewerly/exHarmony huggingface dataset:
A Survey on Vulnerability Assessment &amp; Penetration Testing for Secure Communication

As the technology is growing rapidly, the development of systems and software are becoming more complex. For this reason, the security of software and web applications become more vulnerable. In the last two decades, the use of internet application and security hacking activities are on top of the glance. The organizations are having the biggest challenge that how to secure their web applications from the rapidly increasing cyber threats because the organization can't compromise the security of their sensitive information. Vulnerability Assessment and Penetration Testing techniques may help organizations to find security loopholes. The weakness can be the asset for the attacker if the organizations are not aware of this. Vulnerability Assessment and Penetration Testing helps an organization to cover the security loopholes and determine their security arrangements are working as per defined policies or not. To cover the tracks and mitigate the threats it is necessary to install security patches. This paper includes the survey on the current vulnerabilities, determination of those vulnerabilities, the methodology used for determination, tools used to determine the vulnerabilities to secure the organizations from cyber threat.
Active Appearance Model Induced Generative Adversarial Network for Controlled Data Augmentation

Data augmentation is an important strategy for enlarging training datasets in deep learning-based medical image analysis. This is because large, annotated medical datasets are not only difficult and costly to generate, but also quickly become obsolete due to rapid advances in imaging technology. Image-to-image conditional generative adversarial networks (C-GAN) provide a potential solution for data augmentation. However, annotations used as inputs to C-GAN are typically based only on shape information, which can result in undesirable intensity distributions in the resulting artificially-created images. In this paper, we introduce an active cell appearance model (ACAM) that can measure statistical distributions of shape and intensity and use this ACAM model to guide C-GAN to generate more realistic images, which we call A-GAN. A-GAN provides an effective means for conveying anisotropic intensity information to C-GAN. A-GAN incorporates a statistical model (ACAM) to determine how transformations are applied for data augmentation. Traditional approaches for data augmentation that are based on arbitrary transformations might lead to unrealistic shape variations in an augmented dataset that are not representative of real data. A-GAN is designed to ameliorate this. To validate the effectiveness of using A-GAN for data augmentation, we assessed its performance on cell analysis in adaptive optics retinal imaging, which is a rapidly-changing medical imaging modality. Compared to C-GAN, A-GAN achieved stability in fewer iterations. The cell detection and segmentation accuracy when assisted by A-GAN augmentation was higher than that achieved with C-GAN. These findings demonstrate the potential for A-GAN to substantially improve existing data augmentation methods in medical image analysis.
Spring Hill (NNP-I 1000) Intel’s Data Center Inference Chip

This article consists of a collection of slides from the author's conference presentation.
Restoration Work

There has been a growing commitment across the fields of Computer-Supported Cooperative Work (CSCW) and Human-Computer Interaction for Development (HCI4D) towards investigating the design and deployment of technologies in the context of complex healthcare ecologies. We present a qualitative inquiry of one such context, as we examine the outreach practices and everyday challenges of workers at a community-based organization in Gujarat (India) that works with People Living with the Human Immuno-deficiency Virus(PLHIV). Drawing on Yosso's framework of community cultural wealth-apt for such intersectional settings-and the lens of articulation work, we describe how the workers at Vikas build and strengthen varied forms of capital to restore "old normals," or what life was like for their PLHIV clients prior to diagnoses. Finally, we propose that attention to this nature of restoration work, and the workers' engagement with diverse forms of community cultural wealth, allows us to reflect on how technologies might (or might not) be designed to impact social and affective aspects of health.
Experimental Evaluation of CNN Architecture for Speech Recognition

In recent days, deep learning has been widely used in signal and information processing. Among the deep learning algorithms, Convolution Neural Network (CNN) has been widely used for image recognition and classification because of its architecture, high accuracy and efficiency. This paper proposes a method that uses the CNN on audio samples rather than on the image samples in which the CNN method is usually used to train the model. The one-dimensional audio samples are converted into two-dimensional data that consists of matrix of Mel-Frequency Cepstral Coefficients (MFCCs) that are extracted from the audio samples and the number of windows used in the extraction. This proposed CNN model has been evaluated on the TIDIGITS corpus dataset. The paper analyzes different convolution layer architectures with different number of feature maps in each architecture. The three-layer convolution architecture was found to have the highest accuracy of 97.46% among the other discussed architectures.
Social acceptability of virtual reality in public spaces

With the latest advancements in Virtual Reality (VR), the possible use of VR devices in public and social contexts has increased. Since the use of VR typically requires wearing a Head-Mounted Display (HMD), the user is not able to see others - the spectators - present in the same context. This may lead to a decrease of social acceptability of VR by both the users and the spectators. We conducted a field experiment to explore what are the experiential factors of the users of VR (N=10) and spectators of VR use (N=30). We found experiential factors for the users to be adjustment of interaction, uninterruptable immersion, un-intrusive communication, freedom to switch between realities, sense of safety, physical privacy, shared experience, and sense of belonging. For the spectators, the main factors are shared experience, enticing curiosity, feeling normal, and sense of safety. We then run three sessions with user experience (UX) experts (N=9) to create a set of design recommendations for socially acceptable VR. The resulting ten recommendations provide a holistic view to designing acceptable experiences for VR in public spaces.
Learning-based Physical Layer Communications for Multiagent Collaboration

Consider a collaborative task carried out by two autonomous agents that can communicate over a noisy channel. Each agent is only aware of its own state, while the accomplishment of the task depends on the value of the joint state of both agents. As an example, both agents must simultaneously reach a certain location of the environment, while only being aware of their own positions. Assuming the presence of feedback in the form of a common reward to the agents, a conventional approach would apply separately: (i) an off-the-shelf coding and decoding scheme in order to enhance the reliability of the communication of the state of one agent to the other; and (ii) a standard multiagent reinforcement learning strategy to learn how to act in the resulting environment. In this work, it is argued that the performance of the collaborative task can be improved if the agents learn how to jointly communicate and act. In particular, numerical results for a baseline grid world example demonstrate that the jointly learned policy carries out compression and unequal error protection by leveraging information about the action policy.
Robust Flexible Preserving Embedding

Neighborhood preserving embedding (NPE) has been proposed to encode overall geometry manifold embedding information. However, the class-special structure of the data is destroyed by noise or outliers existing in the data. To address this problem, in this article, we propose a novel embedding approach called robust flexible preserving embedding (RFPE). First, RFPE recovers the noisy data by low-rank learning and obtains clean data. Then, the clean data are used to learn the projection matrix. In this way, the projective learning is totally unaffected by noise or outliers. By encoding a flexible regularization term, RFPE can keep the property of the data points with a nonlinear manifold and be more flexible. RFPE searches the optimal projective subspace for feature extraction. In addition, we also extend the proposed RFPE to a kernel case and propose kernel RFPE (KRFPE). Extensive experiments on six public image databases show the superiority of the proposed methods over other state-of-the-art methods.
High Performance Modular Multiplication for SIDH

The latest research indicates that quantum computers will be realized in the near future. In theory, the computation speed of a quantum computer is much faster than current computers, which will pose a serious threat to current cryptosystems. Post-quantum cryptography (PQC) is a class of cryptography based on underlying mathematical problems that are considered infeasible to crack even with access to a quantum computer. The supersingular isogeny Diffie-Hellman (SIDH) key exchange protocol is a new post-quantum cryptosystem, which offers advantages in reduced secret key length and attack resistance. SIDH is the basis of the supersingular isogeny key encapsulation (SIKE) protocol, which is in the second round of the U.S. National Institute of Standards and Technology (NIST) PQC standardization process. In this article, we propose a new modular multiplication algorithm and a new interleaved hardware architecture for SIDH. Performance results for the proposed modular multiplier using four parameter sets for the prime, p that correspond to the SIKE Round 2 parameter sets show significant advantages in speed.
Improving Web Search Ranking by Incorporating User Behavior Information

We show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We examine alternatives for incorporating feedback into the ranking process and explore the contributions of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We show that incorporating implicit feedback can augment other features, improving the accuracy of a competitive web search ranking algorithms by as much as 31% relative to the original performance.
Performance Improvement of Ordering and Endorsement Phase in Hyperledger Fabric

In the fourth industrial revolution, blockchain is one of the popular technologies. There are many benefits such as decentralization, persistency and security. A blockchain network can be public or private. Among them, the private blockchain model is applied to the business model. It can be a consortium of networks and is efficiently handled using smart contracts. However, there are many problems in applying the blockchain technology to various environments. One of the concerns is that it does not provide enough performance for many transactions [1]. This paper focuses on improving the performance of Hyperledger Fabric which is reported to be the fastest private blockchain platform [2]. Fabric has various component such as chaincode(smart-contract), endorsing peer, ordering service and committing peer. We propose two optimization method about endorsing peer and ordering service. Firstly, we have improved the procedure of read transactions by distinguishing between read and write transactions in the endorsement phase. We achieved 60% better performance than original read transaction processing. Secondly, we create a new consensus protocol without using an external project. We decreased latency by 80% through ordering service optimization. By combining two optimizations, we improved the overall latency and throughput performance by 20%.
Evaluating the user experience of omnidirectional VR walking simulators

Omnidirectional treadmills (ODTs) have been traditionally proposed as a promising solution for users’ navigation in large-scale virtual environments. These mechanical devices enable users to perform locomotive motion with 360-deg freedom, while keeping their position fixed in the physical world. However, most locomotion approaches based on omnidirectional treadmills have presented either high acquisition or maintenance costs, being the capabilities of the general public, or a limited reliability. In this paper, we present a comparative usability and acceptance study with real users evaluating the two most common approaches for the development of this type of Virtual Reality (VR) walking simulators, whether flat-based or bowl-based omnidirectional treadmills. The results of our study indicate that both design alternatives accomplish a similar performance and can be considered as personal, low-cost walking simulators for navigation in virtual environments. Nevertheless, when compared between themselves, the users preferred the bowl-based omnidirectional VR treadmill when following three different selection criteria: overall preference, recommendation, and usefulness. Last, a correlation analysis showed statically significant correspondences among the parameters concerning to the simulation presence, which in turn, are related to the global scores and responses given by the participants of the Virtual Reality experiment.
Influence of random topology in artificial neural networks: A survey

Due to the fully-connected complex structure of Artificial Neural Networks (ANNs), systems based on ANN may consume much computational time, energy and space. Therefore, intense research has been recently centered on changing the topology and design of ANNs to obtain high performance. To explore the influence of network structure on ANNs complex systems topologies have been applied in these networks to have more efficient and less complex structures while they are more similar to biological systems at the same time. In this paper, the methodology and results of some recent papers are summarized and discussed in which the authors investigated the efficacy of random complex networks on the performance of Hopfield associative memory and multi-layer ANNs compared with ANNs with small-world, scale-free and regular structures.
Help, I need somebody: Examining the antecedents of social support seeking among cybercrime victims

An important portion of internet users have faced cybercrime in recent years. One successful strategy for dealing with cybercrime victimization is to seek social support. However, previous studies showed that only a limited number of victims reaches out to family or friends to ask for help after a cybercrime incident. The current study sought to gain a better understanding of victims’ social support seeking by exploring its antecedents. Specifically, the study took into account the role of (1) perception (i.e., perceived severity and perceived control), (2) primary responses (i.e., self-blame and denial), and (3) social capital (i.e., available [trusted] connections). Moreover, we explored the link between fear of cybercrime and these antecedents. Data collected from 334 cybercrime victims were analyzed using structural equation modeling. The findings indicated that victims with high perceived control and who ignore the incident, are less inclined to ask for help. Surprisingly, victims with high levels of self-blame are more likely to seek support. Moreover, we found that fear of crime is significantly related with perception and self-blame. Future awareness campaigns should stress that support seeking is part of the solution and should avoid placing the responsibility of victimization completely on the victim.
Processing knowledge graph-based complex questions through question decomposition and recomposition

Nowadays, question answering (QA) over complex questions is the most spotlighted research topic in diverse communities. Different from existing QA approaches for simple questions that require a single relation in a knowledge graph (KG) to find an answer set, QA for complex questions requires to process multiple KG relations. Complex questions often include some representations for constraints (ordinal, temporal, etc.) that restrict an answer set for the given question. Despite lots of efforts to process such questions, there are still limitations in processing constraints, multiple relations, and variables. To solve the issues, we propose a novel QA method that first decomposes an input question and then generates a correct query graph with fully complete semantics. In order to fill with lossy semantics caused by the decomposition task, the proposed method employs Bi-GRU based model. The model integrates individual compositional semantics of sub-query graphs matched to decomposed sub-questions. Experimental results show the best performance compared to the state-of-art on complex questions.
Blockchain Model for Enhancing Aircraft Maintenance Records Security

This paper addresses the need to enhance the integrity and transparency of aircraft maintenance records in the aviation industry by using blockchain technologies. A physical aircraft maintenance logbook is susceptible to being lost or destroyed. A nonexistent aircraft maintenance logbook hurts the confidence in integrity and reputation of the aircraft. Furthermore, fraud can occur through forgery of Federal Aviation Administration (FAA) personnel signatures and the installation of non-official aircraft parts with implications to homeland security. The scope of this work is to develop a secure blockchain that can store aircraft service records and information in a digital distributed ledger. By keeping the maintenance logbook on a digital ledger, records can be stored indefinitely in a trusted environment with the integrity of records guaranteed. Additionally, to achieve being a distributed ledger, a consensus algorithm Proof of Elapsed Time (PoET) is used to display the global state accurately to all users. The proposed Secure Aircraft Maintenance Records (SAMR) blockchain uses the Linux Foundations open sourced software “Hyperledger” to facilitate an environment that mimics a real-world implementation. The blockchain logic for SAMR implementation was made in Python through creation of a permission-based blockchain for holding the maintenance records.
EXAMINING THE USE BEHAVIOUR OF AUGMENTED REALITY TECHNOLOGY THROUGH MARLCARDIO: ADAPTING THE UTAUT MODEL

Augmented reality (AR) is one type of learning process that can be applied through mobile learning known as mobile augmented reality. AR offers an innovative learning space through an active interaction of superimposing digital contents into the real context to enhance learning experiences. Previous studies have found that AR in education is able to lead students to achieve higher levels of engagement in the learning process. In contrast, the potential of AR in education has not been explored intensively. Therefore, this article is to look into factors that might influence pre-service teachers' use behaviour of Mobile Augmented Reality Learning Cardiovascular (MARLCardio). A self-designed MARLCardio app has been used as an experimental instrument in collecting data. The influencing factors towards use behaviour of MARLCardio are performance expectancy, effort expectancy, social influence and facilitating conditions. This study is a part of an on-going research. In this study, a total of 75 respondents used MARLCardio app before answering a survey questionnaire. The respondents were pre-service teachers at one public university in Malaysia. All data from the questionnaire were analysed using multiple regression analysis. As a result of this investigation, findings showed that effort expectancy is the dominant factor in determining the use behaviour of MARLCardio. Interestingly, performance expectancy did not show any significant relationship.&#x0D; KEYWORDS: Augmented Reality, Mobile Augmented Reality, UTAUT Model, Technology Adoption Model, Use Behaviour
An Explainability Study of the Constant Q Cepstral Coefficient Spoofing Countermeasure for Automatic Speaker Verification

Anti-spoofing for automatic speaker verification is now a well established area of research, with three competitive challenges having been held in the last 6 years. A great deal of research effort over this time has been invested into the development of front-end representations tailored to the spoofing detection task. One such approach known as constant Q cepstral coefficients (CQCCs) have been shown to be especially effective in detecting attacks implemented with a unit selection based speech synthesis algorithm. Despite their success, they largely fail in detecting other forms of spoofing attack where more traditional front-end representations give substantially better results. Similar differences were also observed in the most recent, 2019 edition of the ASVspoof challenge series. This paper reports our attempts to help explain these observations. The explanation is shown to lie in the level of attention paid by each front-end to different sub-band components of the spectrum. Thus far, surprisingly little has been learned about what artefacts are being detected by spoofing countermeasures. Our work hence aims to shed light upon signal or spectrum level artefacts that serve to distinguish different forms of spoofing attack from genuine, bone fide speech. With a better understanding of these artefacts we will be better positioned to design more reliable countermeasures.
Multi-task Learning for Brain Tumor Segmentation

Accurate and reproducible detection of a brain tumor and segmentation of its sub-regions has high relevance in clinical trials and practice. Numerous recent publications have shown that deep learning algorithms are well suited for this application. However, fully supervised methods require a large amount of annotated training data. To obtain such data, time-consuming expert annotations are necessary. Furthermore, the enhancing core appears to be the most challenging to segment among the different sub-regions. Therefore, we propose a novel and straightforward method to improve brain tumor segmentation by joint learning of three related tasks with a partly shared architecture. Next to the tumor segmentation, image reconstruction and detection of enhancing tumor are learned simultaneously using a shared encoder. Meanwhile, different decoders are used for the different tasks, allowing for arbitrary switching of the loss function. In effect, this means that the architecture can partly learn on data without annotations by using only the autoencoder part. This makes it possible to train on bigger, but unannotated datasets, as only the segmenting decoder needs to be fine-tuned solely on annotated images. The second auxiliary task, detecting the presence of enhancing tumor tissue, is intended to provide a focus of the network on this area, and provides further information for postprocessing. The final prediction on the BraTS validation data using our method gives Dice scores of 0.89, 0.79 and 0.75 for the whole tumor, tumor core and the enhancing tumor region, respectively.