Reviewerly/exHarmony huggingface dataset:
Semantically consistent text to fashion image synthesis with an enhanced attentional generative adversarial network

Recent advancements in Generative Adversarial Networks (GANs) have led to significant improvements in various image generation tasks including image synthesis based on text descriptions. In this paper, we present an enhanced Attentional Generative Adversarial Network (e-AttnGAN) with improved training stability for text-to-image synthesis. e-AttnGAN’s integrated attention module utilizes both sentence and word context features and performs feature-wise linear modulation (FiLM) to fuse visual and natural language representations. In addition to multimodal similarity learning for text and image features of AttnGAN [1], similarity and feature matching losses between real and generated images are included while employing classification losses for “significant attributes”. In order to improve the stability of the training and solve the mode collapse issue, spectral normalization and two-time scale update rule are used for the discriminator together with instance noise. Our experiments show that e-AttnGAN outperforms state-of-the-art methods using the FashionGen and DeepFashion-Synthesis datasets in terms of inception score, R-precision and classification accuracy. A detailed ablation study has been conducted to observe the effect of each component.
A blockchain based decentralized data security mechanism for the Internet of Things

The Internet of Things (IoT) is the core infrastructure of the smart city information system. With the explosive growth of IoT devices, how to securely maintain the important data generated by IoT devices has become an important issue. In the conventional IoT-cloud based infrastructure, the sensitive IoT data was stored in a third cloud service provider. However, in such a manner, the private IoT data may be disclosed by the cloud server since the cloud server knows all the data stored in it. This paper, for the first time, proposes a decentralized secure mechanism based on the blockchain technique to store the important data generated in the IoT system. This mechanism effectively solves the data reliability, security and privacy issues that may be encountered in the conventional IoT-cloud system. Considering the defects of simplified payment verification used by light nodes in blockchain networks, this paper proposes an Unspent Transaction Output (UTXO) verification mechanism based on the RSA accumulator, which makes the computational complexity of light nodes to generate and verify the UTXO proof to be constant. The proposed mechanism not only provides proof of inclusion but also supports efficient proof of exclusion for a lightweight node. Our experiment results indicate that the proposed scheme is practical and efficient.
Guaranteed Output Delivery Comes Free in Honest Majority MPC

We study the communication complexity of unconditionally secure MPC with guaranteed output delivery over point-to-point channels for corruption threshold $$t < n/2$$ , assuming the existence of a public broadcast channel. We ask the question: "is it possible to construct MPC in this setting s.t. the communication complexity per multiplication gate is linear in the number of parties?" While a number of works have focused on reducing the communication complexity in this setting, the answer to the above question has remained elusive until now. We also focus on the concrete communication complexity of evaluating each multiplication gate. We resolve the above question in the affirmative by providing an MPC with communication complexity $$O(Cn\phi )$$ bits (ignoring fixed terms which are independent of the circuit) where $$\phi $$ is the length of an element in the field, C is the size of the (arithmetic) circuit, n is the number of parties. This is the first construction where the asymptotic communication complexity matches the best-known semi-honest protocol. This represents a strict improvement over the previously best-known communication complexity of $$O(C(n\phi \,+\,\kappa )\,+\,D_Mn^2\kappa )$$ bits, where $$\kappa $$ is the security parameter and $$D_M$$ is the multiplicative depth of the circuit. Furthermore, the concrete communication complexity per multiplication gate is 5.5 field elements per party in the best case and 7.5 field elements in the worst case when one or more corrupted parties have been identified. This also roughly matches the best-known semi-honest protocol, which requires 5.5 field elements per gate. The above also yields the first secure-with-abort MPC protocol with the same cost per multiplication gate as the best-known semi-honest protocol. Our main result is obtained by compiling the secure-with-abort MPC protocol into a fully secure one.
MAPE-K/MAPE-SAC: An interaction framework for adaptive systems with security assurance cases

Security certification establishes that a given system satisfies properties and constraints as specified in the system security profile. Mechanisms and techniques have been developed to assess if and how well the system complies with the properties, thereby providing a degree of confidence in the security certification. Generally, certification of security controls defined by NIST SP800-53 is performed at design time to provide confidence in a system’s trustworthiness to achieve the organization’s mission and business requirements. Assuring confidence in a self-adaptive system’s security profile is challenging when both functional and security conditions may change at run time. Static security solutions are insufficient, given that dynamic application of defense mechanisms often needs to dynamically adapt security functionality at run time as part of self-protection. This security adaptation may hinder maintaining functional constraints or vice versa. In addition, adaptation capabilities may give rise to the need for dynamic certification, which can be a difficult procedure given the complexity of the security dependencies. Confidence in an information system’s compliance with security constraints can be expressed using security assurance cases (SACs). NIST security controls are defined with a hierarchical structure that makes them amenable to being specified in terms of SACs. A collection of SACs for related security controls form a network that can be used to measure the confidence of security compliance through certification-based evidence. Once the system is deployed, environmental and functional uncertainties may require the coordination of functional and security adaptations. This paper introduces the MAPE-SAC, a security-focused feedback control loop, and its interaction with a MAPE-K, function and performance-focused control loop, to dynamically manage run-time adaptations in response to changes in functional and security conditions. We illustrate the use of both control loops and their interaction with an example of two independent systems that need to cooperate to facilitate autonomous search and rescue in the aftermath of a natural disaster.
Blind Image Quality Measurement by Exploiting High-Order Statistics With Deep Dictionary Encoding Network

Blind image quality measurement (BIQM) has achieved great progress due to the deployment of deep neural networks (DNNs) for training end-to-end models. Most of the existing DNN-based BIQM methods simply aggregate the local deep feature maps with a global max or average pooling layer to generate holistic feature vectors for quality prediction. However, such pooling strategy fails to capture the high-order statistics of local feature descriptors. Inspired by the success of dictionary encoding-based BIQM methods, this article proposes a deep dictionary encoding network (Deep-DEN) that can well capture the high-order statistics of local deep features in an end-to-end manner. In Deep-DEN, dictionary encoding is encapsulated into a single learnable layer attached to the end of a backbone network and followed by a fully connected layer for quality prediction. As a result, high-order statistics of the extracted local deep features in the backbone network and quality prediction functions are simultaneously optimized in a fully supervised manner. The performance of Deep-DEN has been extensively evaluated on several benchmarks and the superiority has been well validated by comparisons with other state-of-the-art BIQM methods.
Key-Policy Attribute-Based Encryption With Keyword Search in Virtualized Environments

Cloud computing is a model for convenient, on-demand network access to virtualized environments of configurable computing resources. It is challenging to search data encrypted and stored in cloud storage servers. Searchable encryption enables data users to search on ciphertext without leaking any information about keywords and the plaintext of the data. Currently, a number of searchable encryption schemes have been proposed, but most of them provide unlimited search privileges to data users, which is not desirable in certain scenarios. In this paper, we propose a new construction of searchable encryption with fine-grained access control by using key-policy attribute-based cryptography to generate trapdoors to support AND, OR and threshold gates. The main idea is that the data owner encrypts the index keywords according to the specified access policy. The data user can generate a trapdoor to search on data, if and only if the attributes of the data user satisfy the access policy. We provide formal security proofs for the scheme, including the indistinguishability of ciphertexts and the indistinguishability of trapdoors, which are used to resist the chosen keyword attack and the keyword guessing attack of external adversaries. Comprehensive security analysis and implementation results show that the proposed scheme is provably secure and feasible in real-world applications.
FDDWNet: A Lightweight Convolutional Neural Network for Real-Time Semantic Segmentation

This paper introduces a lightweight convolutional neural network, called FDDWNet, for real-time accurate semantic segmentation. In contrast to recent advances of lightweight networks that prefer to utilize shallow structure, FDDWNet makes an effort to design more deeper network architecture, while maintains faster inference speed and higher segmentation accuracy. Our network uses factorized dilated depth-wise separable convolutions (FDDWC) to learn feature representations from different scale receptive fields with fewer model parameters. Additionally, FDDWNet has multiple branches of skipped connections to gather context cues from intermediate convolution layers. The experiments show that FDDWNet only has 0.8M model size, while achieves 60 FPS running speed on a single RTX 2080Ti GPU with a 1024 × 512 input image. The comprehensive experiments demonstrate that our model achieves state-of-the-art results in terms of available speed and accuracy trade-off on CityScapes and CamVid datasets.
Facial Emotion Recognition Using Light Field Images with Deep Attention-Based Bidirectional LSTM

Light field cameras are able to capture the intensity of light rays coming from multiple directions, thus representing the visual scene from multiple viewpoints. This paper exploits the rich spatio-angular information available in light field images for facial emotion recognition. In this context, a new deep network is proposed that first extracts spatial features using a VGG16 convolutional neural network. Then, a Bidirectional Long Short-Term Memory (Bi-LSTM) recurrent neural network is used to learn spatio-angular features from viewpoint feature sequences, exploring both forward and backward angular relationships. Additionally, an attention mechanism allows our model to selectively focus on the most important spatio-angular features, thus enabling a more effective learning outcome. Finally, a fusion scheme is adopted to obtain the emotion recognition classification results. Comprehensive experiments have been conducted on the IST-EURECOM Light Field Face database using two challenging evaluation protocols, showing the superiority of our method over the state-of-the-art.
xMP: Selective Memory Protection for Kernel and User Space

Attackers leverage memory corruption vulnerabilities to establish primitives for reading from or writing to the address space of a vulnerable process. These primitives form the foundation for code-reuse and data-oriented attacks. While various defenses against the former class of attacks have proven effective, mitigation of the latter remains an open problem. In this paper, we identify various shortcomings of the x86 architecture regarding memory isolation, and leverage virtualization to build an effective defense against data-oriented attacks. Our approach, called xMP, provides (in-guest) selective memory protection primitives that allow VMs to isolate sensitive data in user or kernel space in disjoint xMP domains. We interface the Xen altp2m subsystem with the Linux memory management system, lending VMs the flexibility to define custom policies. Contrary to conventional approaches, xMP takes advantage of virtualization extensions, but after initialization, it does not require any hypervisor intervention. To ensure the integrity of in-kernel management information and pointers to sensitive data within isolated domains, xMP protects pointers with HMACs bound to an immutable context, so that integrity validation succeeds only in the right context. We have applied xMP to protect the page tables and process credentials of the Linux kernel, as well as sensitive data in various user-space applications. Overall, our evaluation shows that xMP introduces minimal overhead for real-world workloads and applications, and offers effective protection against data-oriented attacks.
A multi-platform dataset for detecting cyberbullying in social media

Recent work on cyberbullying detection relies on using machine learning models with text and metadata in small datasets, mostly drawn from single social media platforms. Such models have succeeded in predicting cyberbullying when dealing with posts containing the text and the metadata structure as found on the platform. Instead, we develop a multi-platform dataset that consists purely of the text from posts gathered from seven social media platforms. We present a multi-stage and multi-technique annotation system that initially uses crowdsourcing for post and hashtag annotation and subsequently utilizes machine-learning methods to identify additional posts for annotation. This process has the benefit of selecting posts for annotation that have a significantly greater than chance likelihood of constituting clear cases of cyberbullying without limiting the range of samples to those containing predetermined features (as is the case when hashtags alone are used to select posts for annotation). We show that, despite the diversity of examples present in the dataset, good performance is possible for models trained on datasets produced in this manner. This becomes a clear advantage compared to traditional methods of post selection and labeling because it increases the number of positive examples that can be produced using the same resources and it enhances the diversity of communication media to which the models can be applied.
Enhanced Attention Using Head-mounted Virtual Reality

Some evidence suggests that experiencing a given scenario using virtual reality (VR) may engage greater attentional resources than experiencing the same scenario on a 2D computer monitor. However, the underlying neural processes associated with these VR-related effects, especially those pertaining to current consumer-friendly head-mounted displays of virtual reality (HMD-VR), remain unclear. Here, two experiments were conducted to compare task performance and EEG-based neural metrics captured during a perceptual discrimination task presented on two different viewing platforms. Forty participants (20-25 years old) completed this task using both an HMD-VR and traditional computer monitor in a within-group, randomized design. Although Experiment I (n = 20) was solely behavioral in design, Experiment II (n = 20) utilized combined EEG recordings to interrogate the neural correlates underlying potential performance differences across platforms. These experiments revealed that (1) there was no significant difference in the amount of arousal measured between platforms and (2) selective attention abilities in HMD-VR environment were enhanced from both a behavioral and neural perspective. These findings suggest that the allocation of attentional resources in HMD-VR may be superior to approaches more typically used to assess these abilities (e.g., desktop/laptop/tablet computers with 2D screens).
The Role of Plausibility in the Experience of Spatial Presence in Virtual Environments

In the present study, we examine the effect of plausibility violations in a virtual environment (VE) on spatial presence. After reviewing research on the association between plausibility and spatial presence, we present a dual-systems approach to understanding the effect of plausibility (or violations thereof) on spatial presence. We conceptualize the feeling of being present in a VE as a lower-order cognitive process. Perceptions of plausibility violations might represent higher-order cognitive processes that could interfere with spatial presence. We present data from an experimental study in which we manipulated the external consistency (i.e., the plausibility) of the VE, cognitive load (to interfere with higher-order cognitive processes), and immersion to examine its effect on spatial presence. Results show that immersion was the most important factor driving the presence experience. We found no difference between low and high plausibility in spatial presence. Subsequent equivalence tests showed that the group exposed to the implausible VE did not feel less present than the group exposed to the plausible VE. We discuss the findings of our studies in the light of our theoretical considerations and previous research.
Multi-source social media data sentiment analysis using bidirectional recurrent convolutional neural networks

Subjectivity detection in the text is essential for sentiment analysis, which requires many techniques to perceive unanticipated means of communication. Few accomplishments adapted to capture the syntactic, semantic, and contextual sentimental information via distributed word representations (DWRs)1 . This paper, concatenating the DWRs through a weighted mechanism on Recurrent Neural Network (RNN) variants joint with Convolutional Neural network (CNN) distinctively involving weighted attentive pooling (WAP)2 . Whereas, CNNs with traditional pooling operations comprise many layers merely able to capture enough features. Our considerations empower the sentiment analysis over DWRs contains Word2vec, FastText, and GloVe to produce dense efficient concatenated representation (DECR)3 to hold long term dependencies on a single RNN layer acquired by Parts of Speech Tagging (POS) explicitly with verbs, adverbs, and noun only. Then use these representations gained in a way, inputted to CNN contain single convolution layer engaging WAP on multi-source social media data to handle the issues of syntactic and semantic regularities as well as out of vocabulary (OOV) words. Experimentations demonstrate that DWRs together with proposed concatenation qualified in resolving the mentioned issues by moderate hyper-parameter configurations. Our architecture devoid of stacking multiple layers achieved modest accuracy of 89.67% by DECR-Bi-GRU-CNN (WAP) on IMDB as compared to random initialization 81.11% on SST.
Evaluations on Deep Neural Networks Training Using Posit Number System

The training of Deep Neural Networks (DNNs) brings enormous memory requirements and computational complexity, which makes it a challenge to train DNN models on resource-constrained devices. Training DNNs with reduced-precision data representation is crucial to mitigate this problem. In this article, we conduct a thorough investigation on training DNNs with low-bit posit numbers, a Type-III universal number (Unum). Through a comprehensive analysis of quantization with various data formats, it is demonstrated that the posit format shows great potential to be employed in the training of DNNs. Moreover, a DNN training framework using 8-bit posit is proposed with a novel tensor-wise scaling scheme. The experiments show the same performance as the state-of-the-art (SOTA) across multiple datasets (MNIST, CIFAR-10, ImageNet, and Penn Treebank) and model architectures (LeNet-5, AlexNet, ResNet, MobileNet-V2, and LSTM). We further design an energy-efficient hardware prototype for our framework. Compared to the standard floating-point counterpart, our design achieves a reduction of 68, 51, and 75 percent in terms of area, power, and memory capacity, respectively.
CALL research: Where are we now?

Abstract This article considers the current position of computer-assisted language learning (CALL) research by producing an integrative synthetic overview of all the articles published in three leading international CALL journals: ReCALL (in its 31st year of publication), the CALICO Journal (its 36th) and Computer Assisted Language Learning journal (its 32nd) over a sustained recent period: 2006–2016. They are judged sufficiently representative to enable broad trends to be detected and the sector’s strengths and weaknesses to be identified. The focus is on CALL research’s international reach, the range of topics researched and the nature of the studies themselves. The findings suggest that CALL research is growing internationally in the number of countries and researchers involved. A wide range of topics is researched, but there is a concentration of papers published on a cluster of popular areas. Consequently, fewer articles are published on a large number of CALL topics or, in some cases, rarely studied. The research methods employed are rigorous: in writing, structure, theory, literature awareness, and discussion and presentation of results, yet there are still weaknesses. Most empirical studies are small scale: based on one institution, a small cohort of students, over a short period of time and seldom followed up. Based on these findings, suggestions are made with a view to broadening and strengthening CALL research through targeting neglected strategic areas with special journal issues and conferences, and improving the quality of research projects. Key areas for future research are proposed.
Deep Cross-Modal Hashing With Hashing Functions and Unified Hash Codes Jointly Learning

Due to their high retrieval efficiency and low storage cost, cross-modal hashing methods have attracted considerable attention. Generally, compared with shallow cross-modal hashing methods, deep cross-modal hashing methods can achieve a more satisfactory performance by integrating feature learning and hash codes optimizing into a same framework. However, most existing deep cross-modal hashing methods either cannot learn a unified hash code for the two correlated data-points of different modalities in a database instance or cannot guide the learning of unified hash codes by the feedback of hashing function learning procedure, to enhance the retrieval accuracy. To address the issues above, in this paper, we propose a novel end-to-end Deep Cross-Modal Hashing with Hashing Functions and Unified Hash Codes Jointly Learning (DCHUC). Specifically, by an iterative optimization algorithm, DCHUC jointly learns unified hash codes for image-text pairs in a database and a pair of hash functions for unseen query image-text pairs. With the iterative optimization algorithm, the learned unified hash codes can be used to guide the hashing function learning procedure; Meanwhile, the learned hashing functions can feedback to guide the unified hash codes optimizing procedure. Extensive experiments on three public datasets demonstrate that the proposed method outperforms the state-of-the-art cross-modal hashing methods.