Reviewerly/exHarmony huggingface dataset:
Blind Image Denoising via Dynamic Dual Learning

Existing discriminative learning methods for image denoising use either a single residual learning or a nonresidual learning design. However, we observe that these two schemes perform differently with the same noise level, and yet, there have been no explorations regarding whether residual or nonresidual designs are better suited for denoising. Additionally, many discriminative denoisers are designed to learn a model that corresponds to a fixed noise level, which means that multiple models are required to recover corrupted images with noise at different levels. In this paper, we propose a dynamic dual learning network for blind image denoising, namely, DualBDNet. Instead of modeling a sole task prediction network, the proposed DualBDNet investigates the inherent relations between the residual estimation and the nonresidual estimation. In particular, DualBDNet produces task-dependent feature maps, and each part of the features is devoted to one specific task (residual/nonresidual mapping). To address different noise levels with a single network or even cases where the statistics of noise are unknown, we further introduce an embedded subnetwork into DualBDNet. One output of the subnetwork is the learning of a dynamic compositional attention to highlight the more significant task-dependent feature maps, adaptively coinciding with the extent of corruption. The other output is the learning of a weight used for fusion of the results to ensure an end-to-end manner. Extensive experiments demonstrate that the proposed DualBDNet outperforms the state-of-the-art methods on both synthetic and real noisy images without estimating the noise levels as input.
Scripted Video Generation With a Bottom-Up Generative Adversarial Network

Generating videos given a text description (such as a script) is non-trivial due to the intrinsic complexity of image frames and the structure of videos. Although Generative Adversarial Networks (GANs) have been successfully applied to generate images conditioned on a natural language description, it is still very challenging to generate realistic videos in which the frames are required to follow both spatial and temporal coherence. In this paper, we propose a novel Bottom-up GAN (BoGAN) method for generating videos given a text description. To ensure the coherence of the generated frames and also make the whole video match the language descriptions semantically, we design a bottom-up optimisation mechanism to train BoGAN. Specifically, we devise a region-level loss via attention mechanism to preserve the local semantic alignment and draw details in different sub-regions of video conditioned on words which are most relevant to them. Moreover, to guarantee the matching between text and frame, we introduce a frame-level discriminator, which can also maintain the fidelity of each frame and the coherence across frames. Last, to ensure the global semantic alignment between whole video and given text, we apply a video-level discriminator. We evaluate the effectiveness of the proposed BoGAN on two synthetic datasets (i.e., SBMG and TBMG) and two real-world datasets (i.e., MSVD and KTH).
CAxCNN: Towards the Use of Canonic Sign Digit Based Approximation for Hardware-Friendly Convolutional Neural Networks

The design of hardware-friendly architectures with low computational overhead is desirable for low latency realization of CNN on resource-constrained embedded platforms. In this work, we propose CAxCNN, a Canonic Sign Digit (CSD) based approximation methodology for representing the filter weights of pre-trained CNNs.The proposed CSD representation allows the use of multipliers with reduced computational complexity. The technique can be applied on top of state-of-the-art CNN quantization schemes in a complementary manner. Our experimental results on a variety of CNNs, trained on MNIST, CIFAR-10 and ImageNet datasets, demonstrate that our methodology provides CNN designs with multiple levels of classification accuracy, without requiring any retraining, and while having a low area and computational overhead. Furthermore, when applied in conjunction with a state-of-art quantization scheme, CAxCNN allows the use of multipliers, which offer 77% logic area reduction, as compared to their accurate counterpart, while incurring a drop in Top-1 accuracy of just 5.63% for a VGG-16 network trained on ImageNet.
Towards the Detection of Phishing Attacks

Phishing is an act of creating a website similar to a legitimate website with a motive of stealing user's confidential information. Phishing fraud might be the most popular cybercrime. Phishing is one of the risks that originated a couple of years back but still prevailing. This paper discusses various phishing attacks, some of the latest phishing evasion techniques used by attackers and anti-phishing approaches. This review raises awareness of those phishing strategies and helps the user to practice phishing prevention. Here, a hybrid approach of phishing detection also described having fast response time and high accuracy.
COVID-19 Contact Tracing Solutions for Mass Gatherings

ABSTRACT Mass gatherings and high-density activities, such as sporting events, conventions, and theme parks, are consistently included among highest-risk activities given the increased potential for widespread coronavirus disease 2019 (COVID-19) transmission. A more balanced risk management approach is required because absolute suppression of risk is unrealistic in all facets of life. Contact tracing remains a limiting factor in achieving such a balance. The use of Bluetooth or pairing devices is proposed to address this challenge. This simple approach, when applied in a manner that satisfies privacy and trust concerns, would allow high-risk encounters to be quickly identified, namely those where participants have spent 15 minutes or more within 6 ft of each other per current guidelines. If an attendee later tests positive for COVID-19 and tracing is required, the event organizer can provide a limited list of potential close contacts rather than an exhaustive list of all attendees. Contact tracers can, therefore, limit efforts to this concise group rather than needing to contact thousands of people or conduct mass media communications. Such a system, if institutionalized, supports risk assurance and safety measures for businesses by demonstrating a commitment to staff, customer protection, and ensuring high-risk encounters are logged, reinforcing longer-term societal pandemic resilience.
A Survey on Steganography using Least Significant bit (LSB) Embedding Approach

The way of keeping the existence of the message secret is known as steganography. It is the process of hiding secret information inside a data source. It has many applications in different platforms such as medical, online transactions, defence and many more. It is utilized in circumstances where the secrecy of data is required in communication. In steganography, data can be covered up in content arrangement, images, audio and video. It depends on the quality of a steganographic procedure that lies in its ability to keep the message, as secret as could reasonably be expected and furthermore the measure of information that can be covered up, as enormous as could be allowed. This paper gives a survey on the LSB approach used in this area. Here, LSB technique is used for image, audio as well as video steganography.
A Promising Path Towards Autoformalization and General Artificial Intelligence

An autoformalization system is an AI that learns to read natural language content and to turn it into an abstract, machine verifiable formalization, ideally by bootstrapping from unlabeled training data with minimum human interaction. This is a difficult task in general, one that would require strong automated reasoning and automated natural language processing capabilities. In this paper, it is argued that autoformalization is a promising path for systems to learn sophisticated, general purpose reasoning in all domains of mathematics and computer science. This could have far reaching implications not just for mathematical research, but also for software synthesis. Here I provide the outline for a realistic path towards those goals and give a survey of recent results that support the feasibility of this direction.
Paraphrase Detection Using Deep Neural Network Based Word Embedding Techniques

This paper focuses on detecting paraphrase in sentences using different word vectorization techniques and finding which vectorization method is more efficient. Word vectorization is a technique which is used to retrieve information from large collection of textual data like corpus or documents by associating each word as a vector. As the textual data are massive, the problem with the text data is that it need to defined in a form of numbers for solving mathematical problems. There are elementary to composite methods to solve this problem. In this paper we are comparing different word vectorization techniques and they are, Count Vectorizer,Hashing Vectorizer, TF-IDF Vectorizer, fastText, ELMo, GloVe, BERT.
Persuasive Synthetic Speech

Previous research indicates that synthetic speech can be as persuasive as human speech. However, there is a lack of empirical validation on interactive goal-oriented tasks. In our two-stage study (online listening test and lab evaluation), we compared participants' perception of the persuasiveness of synthetic voices created from speech in a debating style vs. speech from audio-books. Participants interacted with our Conversational Agent (CA) to complete 4 flight-booking tasks and were asked to evaluate the voice, message and perceived personal qualities. We found that participants who interacted with the CA using the voice created from debating style speech rated it as significantly more truthful and more involved than the CA using the audio-book-based voice. However, there was no difference in how frequently each group followed the CA's recommendations. We hope our investigation will provoke discussion about the impact of different synthetic voices on users' perceptions of CAs in goal-oriented tasks.
Intentions, Meanings, and Whys

If an artwork could talk, what would visitors ask? This paper explores what types of content voice-based AI conversational systems should have to attend visitors' expectations in a museum. The study analyses 142,463 conversation logs from 5,242 unique sessions of a nine-month long deployment of a voice-based interactive guide in a modern art museum in Brazil. In this experiment, visitors freely asked questions about seven different artworks of different styles. By grouping the visitor utterances into eight types of content, we determined that more than half of the visitors asked about the meanings and intentions behind the artwork, followed by facts about the artwork and author-related questions. We also determined that the types of questions were not affected by each artwork, the artwork style, or its physical location. We also saw some relationships between the visitor's overall evaluation of the experience with the types of questions she asked. Based on those results, we identified implications for designing content for voice-based conversational systems in museums.
c-Axis orientation determination of α-titanium using Computational Polarized Light Microscopy

Uniaxial materials such as α-titanium exhibit anisotropic optical properties when illuminated using polarized light, i.e., individual grains in a polycrystalline sample reflect different light intensities depending on their orientation. By analyzing the reflected intensities to extract the c-axis orientation of the α-titanium grains, orientation mapping can be carried out. We report on a forward model that was developed to predict the observed images formed from the reflection of polarized light from the α-titanium sample surface. Compared to electron back-scattered diffraction (EBSD), the Computational Polarized Light Microscopy (CPLM) method is low cost, easy to use, and able to accommodate large samples, making it a technique suitable for high-throughput texture analysis.
CHEx86: Context-Sensitive Enforcement of Memory Safety via Microcode-Enabled Capabilities

This work introduces the CHEx86 processor architecture for securing applications, including legacy binaries, against a wide array of security exploits that target temporal and spatial memory safety vulnerabilities such as out-of-bounds accesses, use-after-free, double-free, and uninitialized reads, by instrumenting the code at the microcode-level, completely under-the-hood, with only limited access to source-level symbol information. In addition, this work presents a novel scheme for speculatively tracking pointer arithmetic and pointer movement, including the detection of pointer aliases in memory, at the machine code-level using a configurable set of automatically constructed rules. This architecture outperforms the address sanitizer, a state-of-the-art software-based mitigation by 59%, while eliminating porting, deployment, and verification costs that are invariably associated with recompilation.
Implementation of a canonical phase measurement with quantum feedback

Much of modern metrology and communication technology encodes information in electromagnetic waves, typically as an amplitude or phase. While current hardware can perform near-ideal measurements of photon number or field amplitude, to date no device exists that can even in principle perform an ideal phase measurement. In this work, we implement a single-shot canonical phase measurement on a one-photon wave packet, which surpasses the current standard of heterodyne detection and is optimal for single-shot phase estimation. By applying quantum feedback to a Josephson parametric amplifier, our system adaptively changes its measurement basis during photon arrival and allows us to validate the detector's performance by tracking the quantum state of the photon source. These results provide an important capability for optical quantum computing, and demonstrate that quantum feedback can both enhance the precision of a detector and enable it to measure new classes of physical observables.
Block-Gaussian-Mixture Priors for Hyperspectral Denoising and Inpainting

This article proposes a denoiser for hyperspectral (HS) images that consider, not only spatial features, but also spectral features. The method starts by projecting the noisy (observed) HS data onto a lower dimensional subspace and then learns a Gaussian mixture model (GMM) from 3-D patches or blocks extracted from the projected data cube. Afterward, the minimum mean squared error (MMSE) estimates of the blocks are obtained in closed form and returned to their original positions. Experiments show that the proposed algorithm is able to outperform other state-of-the-art methods under Gaussian and Poissonian noise and to reconstruct high-quality images in the presence of stripes.
Generalized wireless adversarial deep learning

Deep learning techniques can classify spectrum phenomena (e.g., waveform modulation) with accuracy levels that were once thought impossible. Although we have recently seen many advances in this field, extensive work in computer vision has demonstrated that an adversary can "crack" a classifier by designing inputs that "steer" the classifier away from the ground truth. This paper advances the state of the art by proposing a generalized analysis and evaluation of adversarial machine learning (AML) attacks to deep learning systems in the wireless domain. We postulate a series of adversarial attacks, and formulate a Generalized Wireless Adversarial Machine Learning Problem (GWAP) where we analyze the combined effect of the wireless channel and the adversarial waveform on the efficacy of the attacks. We extensively evaluate the performance of our attacks on a state-of-the-art 1,000-device radio fingerprinting dataset, and a 24-class modulation dataset. Results show that our algorithms can decrease the classifiers' accuracy up to 3x while keeping the waveform distortion to a minimum.
Identifying Ethnics of People through Face Recognition: A Deep CNN Approach

The interest in face recognition studies has grown rapidly in the last decade. One of the most important problems in face recognition is the identification of ethnics of people. In this study, a new deep learning convolutional neural network is designed to create a new model that can recognize the ethnics of people through their facial features. The new dataset for ethnics of people consists of 3141 images collected from three different nationalities. To the best of our knowledge, this is the first image dataset collected for the ethnics of people and that dataset will be available for the research community. The new model was compared with two state-of-the-art models, VGG and Inception V3, and the validation accuracy was calculated for each convolutional neural network. The generated models have been tested through several images of people, and the results show that the best performance was achieved by our model with a verification accuracy of 96.9%.
Spatial attention based visual semantic learning for action recognition in still images

Visual semantic parts play crucial roles in still image-based action recognition. A majority of existing methods require additional manual annotations such as human bounding boxes and predefined body parts besides action labels to learn action related visual semantic parts. However, labeling these manual annotations is rather time-consuming and labor-intensive. Moreover, not all manual annotations are effective when recognizing a specific action. Some of them can be irrelevant and even misguided. To address these limitations, this paper proposes a multi-stage deep learning method called Spatial Attention based Action Mask Networks (SAAM-Nets). The proposed method does not need any additional annotations besides action labels to obtain action-specific visual semantic parts. Instead, we propose a spatial attention layer injected in a convolutional neural network to create a specific action mask for each image with only action labels. Moreover, based on the action mask, we propose a region selection strategy to generate a semantic bounding box containing action-specific semantic parts. Furthermore, to effectively combine the information of the whole scene and the sematic box, two feature attention layers are adopted to obtain more discriminative representations. Experiments on four benchmark datasets have demonstrated that the proposed method can achieve promising performance compared with state-of-the-art methods.
Generalized Class Incremental Learning

Many real-world machine learning systems require the ability to continually learn new knowledge. Class incremental learning receives increasing attention recently as a solution towards this goal. However, existing methods often introduce some assumptions to simplify the problem setting, which rarely holds in real-world scenarios. In this paper, we formulate a Generalized Class Incremental Learning (GCIL) framework to systematically alleviate these restrictions, and introduce several novel realistic incremental learning scenarios. In addition, we propose a simple yet effective method, namely ReMix, which combines Exemplar Replay (ER) and Mixup to deal with different challenges in realistic GCIL setups. We demonstrate on CIFAR-100 that ReMix outperforms the state-of-the-art methods in different GCIL setups by significant margins without introducing additional computation cost.
Understanding cloud-based BYOD information security protection behaviour in smart business: in perspective of perceived value

(2021). Understanding cloud-based BYOD information security protection behaviour in smart business: in perspective of perceived value. Enterprise Information Systems: Vol. 15, Cloud & Fog Computing: Intelligent Applications, pp. 1216-1237.
BLAZE: Practical Lattice-Based Blind Signatures for Privacy-Preserving Applications

Blind signatures constitute basic cryptographic ingredients for privacy-preserving applications such as anonymous credentials, e-voting, and Bitcoin. Despite the great variety of cryptographic applications blind signatures also found their way in real-world scenarios. Due to the expected progress in cryptanalysis using quantum computers, it remains an important research question to find practical and secure alternatives to current systems based on the hardness of classical security assumptions such as factoring and computing discrete logarithms. In this work we present BLAZE: a new practical blind signature scheme from lattice assumptions. With respect to all relevant efficiency metrics BLAZE is more efficient than all previous blind signature schemes based on assumptions conjectured to withstand quantum computer attacks. For instance, at approximately 128 bits of security signatures are as small as 6.6 KB, which represents an improvement factor of 2.7 compared to all previous candidates, and an expansion factor of 2.5 compared to the NIST PQC submission $$\textsf {Dilithium}$$ . Our software implementation demonstrates the efficiency of BLAZE to be deployed in practical applications. In particular, generating a blind signature takes just 18 ms. The running time of both key generation and verification is in the same order as state-of-the-art ordinary signature schemes.
Application of Convolutional Neural Network Based on Transfer Learning for Garbage Classification

In order to effectively utilize garbage resources, reduce environmental pollution and the burden of people sorting garbage, this paper proposes a method of garbage classification and recognition based on transfer learning, which migrates the existing InceptionV3 model recognition task on the Imagenet dataset to garbage identification. First, increase the data set through data augmentation. Then build a convolutional neural network based on the source model and adjust the neural network parameters based on the training effect. The training results show that the training accuracy is 99.3% and the test accuracy is 93.2%. Finally, the model is applied to the pictures collected in real life for recognition. The recognition results show that the model has good performance and high accuracy, can correctly identify common garbage in life, and has reference significance for intelligent garbage classification, which proves the feasibility of this method.