Reviewerly/exHarmony huggingface dataset:
Finely Tunable Bitcuboid-Based Encryption With Exception-Free Signed Binarization for JPEG Standard

We propose a finely tunable JPEG format-compliant perceptual encryption (FE) with two novel strategies: (i) bitcuboid-based encryption (BE) and (ii) exception-free signed binarization (ESB). BE is an intra- and inter-bitplane encryption technique that provides finely tunable perceptual degradation by encrypting constrained subspaces (‘bitcubes’) of a cuboid-shaped bit set (‘bitcuboid’). ESB is a binarization technique that redecimalizes encrypted binary sequences into signed decimal coefficients without any exception-handling by shifting the negative binary sequences one-by-one. BE with ESB (BEESB) is applied to the quantized discrete cosine transform (QDCT) domain in JPEG compression. The results of our first experiment show that the BE attains fine tunability, which means scalability of the perceptual degradation level with a single encryption method, by encrypting bitcubes of various types and sizes combinatorially. The results of our second experiment show that the BEESB suppresses the bitrate overheads and that BEESB with one of the most secure options suppresses approximately 0.80-187.58% more of the bitrate overheads in terms of Bjøntegaard delta (BD)-rate compared with conventional methods except for some ones. The results of our third experiment show that BEESB has high resilience against attacks.
Length-constrained Neural Machine Translation using Length Prediction and Perturbation into Length-aware Positional Encoding

Neural machine translation often suffers from an under-translation problem owing to its limited modeling of the output sequence lengths. In this study, we propose a novel approach to training a Transformer model using length constraints based on length-aware positional encoding (PE). Because length constraints with exact target sentence lengths degrade the translation performance, we add a random perturbation with a uniform distribution within a certain range to the length constraints in the PE during the training. In the inference step, we predicted the output lengths from the input sequences using a length prediction model based on a large-scale pre-trained language model. In Japanese-to-English and English-to-Japanese translation, experimental results show that the proposed perturbation injection improves the robustness of the length prediction errors, particularly within a certain range.
Impact of Swear and Negative Texts on Social Media Users

Nowadays, the use of social media has increased exponentially. People show different behavior on social media depending on the kind of responses and behavior of people around them. It is important now to analyze the behavior of social media users and the way how they affect their friends. In this paper, behavioral analysis of people is done based on Twitter data. An algorithm is proposed which helps in finding the impact of text written by someone on social media and its effect on others. The impact of written text is calculated with the help of the number of retweets done for the same tweet. The severity of the used word is calculated based on AFINN dictionary. According to the proposed algorithm, the score of the dictionary is recalculated when a negative word is forwarded multiple times. This is done with the understanding that if a less severe negative word is used many times, it may affect the person in a highly negative manner. With this, Severity of words is recalculated and its impact on people is found with the help of the proposed algorithm. The impact of using negative words on social media affect 32 % of the total users (in their friend-list). Behavior change is demonstrated with the help of graphs week-wise, month-wise and year-wise analyses. The research helps in finding the impact of swear words on social media users depending on the frequency and severity score of the words.
Linear temporal justification logics with past and future time modalities

Abstract Temporal justification logic is a new family of temporal logics of knowledge in which the knowledge of agents is modelled using a justification logic. In this paper, we present various temporal justification logics involving both past and future time modalities. We combine Artemov’s logic of proofs with linear temporal logic with past, and we also investigate several principles describing the interaction of justification and time. We present two kinds of semantics for our temporal justification logics, one based on interpreted systems and Fitting models and the other based on Mkrtychev models, and further, we establish soundness and completeness. We show that the internalization property holds in some of the temporal justification logics. We further investigate the two well-known epistemic-temporal notions of no forgetting and no learning in the framework of justification logics. Finally, we present temporal justification logics that avoid the logical omniscience problem.
Exposing DeepFakes via Localizing the Manipulated Artifacts

In recent years, DeepFake has become a public concern due to the abuse of advanced generative adversarial networks (GANs). Researchers have proposed various approaches to fight against DeepFakes by identifying whether an image or video is synthesized by GANs. Due to the imperfect design of GANs, the introduced artifacts serve as a promising clue for detection, which is captured by many proposed methods. However, these methods failed in presenting the artifacts in an interpretable manner. In this paper, we propose a novel approach by focusing on the artifact regions with dual attention (channel attention and spatial attention) to localize the observable and invisible artifacts for assisting DeepFake detection. Specifically, our proposed approach is agnostic to the specific backbone, which could be easily plugged into any DNN models to improve their performance. Experimental results show that our proposed dual attention could be deployed in any DNN based classifiers to improve their performance in detecting various DeepFakes. The detection accuracy on six current open-source DeepFake datasets is improved by 3.50\(\%\), 2.56\(\%\), 1.64\(\%\), 1.36\(\%\), and 0.89\(\%\) in average on MesoNet, Meso-Inception, VGG-19, Xception, and EfficientNet, respectively. Besides, experimental results also show that our attention mechanism can serve as an asset for pixel-wise manipulation localization.
Optimized bitrate ladders for adaptive video streaming with deep reinforcement learning

In the adaptive video streaming scenario, videos are pre-chunked and pre-encoded according to a set of resolution-bitrate/quality pairs on the server-side, namely bitrate ladder. Hence, we propose DeepLadder, which adopts state-of-the-art deep reinforcement learning (DRL) method to optimize the bitrate ladder by considering video content features, current network capacities, as well as the storage cost. Experimental results on both Constant Bitrate (CBR) and Variable Bitrate (VBR)-encoded videos demonstrate that DeepLadder significantly improvements on average video quality, bandwidth utilization, and storage overhead in comparison to prior work.
Investigating Overparameterization for Non-Negative Matrix Factorization in Collaborative Filtering

Overparameterization is one of the key techniques in modern machine learning, where a model with the higher complexity can generalize better on test data against the common knowledge of the bias-variance trade-off in classical statistical learning theory. In this paper, we empirically investigate the effect of overparameterization for matrix factorization-based models in collaborative filtering. Surprisingly, we firstly show that the performance of overparameterized non-negative matrix factorization (NMF) on test data gets better than that of the underparameterized NMF, which is commonly used to date, and is even competitive with the state-of-the-art collaborative filtering techniques. Moreover, we also show that the double descent phenomenon occurs when we increase the number of parameters of the NMF, where the test error decreases, increases, and decreases again as the model complexity grows, which has been recently reported in various machine learning methods such as deep learning models and kernel methods.
Question Generation via Multi-stage Answers Editing Network

Automatic question generation from a sentence or paragraph is a challenging task. Recently, impressive progress has been made, mainly owing to the advance of deep neural networks. However, existing neural approaches tend to simply copy words from the given answer, making the generated question equivocal and informatively redundant. In this work, we propose an edit-based network, named Multi-stage Answers Editing Network (MultiEdit), to generate the question in the three-stage operations. In the initial stage, we convert the answer into an edit vector and then combine the edit vector and sentence representation to initialize the decoder, which helps choose an interrogative word matching the answer type. In the second stage, we edit the contextual representation by computing the weighted average between the context representation and the edit vector. In the final stage, we adopt the additional reinforcement learning to improve the effectiveness of generated semantics and evaluation indicators. Experimental results on both Chinese and English datasets show that our approach outperforms the state-of-the-art methods in terms of both automatic and human evaluation.
BISWSRBS: A Winograd-based CNN Accelerator with a Fine-grained Regular Sparsity Pattern and Mixed Precision Quantization

Field-programmable Gate Array (FPGA) is a high-performance computing platform for Convolution Neural Networks (CNNs) inference. Winograd algorithm, weight pruning, and quantization are widely adopted to reduce the storage and arithmetic overhead of CNNs on FPGAs. Recent studies strive to prune the weights in the Winograd domain, however, resulting in irregular sparse patterns and leading to low parallelism and reduced utilization of resources. Besides, there are few works to discuss a suitable quantization scheme for Winograd. In this article, we propose a regular sparse pruning pattern in the Winograd-based CNN, namely, Sub-row-balanced Sparsity (SRBS) pattern, to overcome the challenge of the irregular sparse pattern. Then, we develop a two-step hardware co-optimization approach to improve the model accuracy using the SRBS pattern. Based on the pruned model, we implement a mixed precision quantization to further reduce the computational complexity of bit operations. Finally, we design an FPGA accelerator that takes both the advantage of the SRBS pattern to eliminate low-parallelism computation and the irregular memory accesses, as well as the mixed precision quantization to get a layer-wise bit width. Experimental results on VGG16/VGG-nagadomi with CIFAR-10 and ResNet-18/34/50 with ImageNet show up to 11.8×/8.67× and 8.17×/8.31×/10.6× speedup, 12.74×/9.19× and 8.75×/8.81×/11.1× energy efficiency improvement, respectively, compared with the state-of-the-art dense Winograd accelerator [20] with negligible loss of model accuracy. We also show that our design has 4.11× speedup compared with the state-of-the-art sparse Winograd accelerator [19] on VGG16.
Unsupervised Dynamic Network Embedding Using Global Information

Network embedding has become a fascinating research subject in recent years owing to its ability to represent networks with rich relationships in the low-dimensional vector space, which inspires various downstream tasks, such as link prediction and node classification. Nevertheless, most existing network embedding methods focus on static networks where nodes and edges do not evolve with time. Although some methods consider the dynamics of networks, they pay little attention to the global information of networks, or have recourse to node labels for training. In this paper, we propose an unsupervised dynamic network embedding using the global information, called UDNGI. More specifically, we first maximize the mutual information between the local node embedding and the global network embedding based on a well-designed graph convolutional network for capturing the global information at a time-step specific snapshot network. Then, a temporal smoothness constraint is proposed to minimize the embedding deviation between two successive snapshots, and a modified long short-term memory is designed to update the weight parameters of the graph convolutional network, which enables the model to capture the global information across all time steps. Extensive experiments on node classification and link prediction demonstrate that UDNGI achieves a generally better performance than state-of-the-art methods.
Variational Autoencoder Inverse Mapper: An End-to-End Deep Learning Framework for Inverse Problems

Inverse problems - using measured observations to determine unknown parameters - are well motivated but challenging in many science and engineering problems. In this paper, we propose an end-to-end deep learning framework, the Variational Autoencoder Inverse Mapper (VAIM), as an autoencoder-based neural network architecture for inverse problems. The encoder and decoder neural networks approximate the forward and backward mapping, respectively, and a variational latent layer is incorporated into VAIM to learn the posterior parameter distributions with respect to given observables. We demonstrate the effectiveness of VAIM for several toy inverse problems, with both finite and infinite solutions, and for constructing the inverse function mapping quantum correlation functions to observables in a Quantum Chromodynamics analysis of nucleon structure.
An Universal Adversarial Attack Method Based on Spherical Projection

Adversarial attack on neural networks has become an important problem restricting its security applications, and among adversarial attacks oriented towards the sample set, the universal perturbation design causing most sample output errors is critical to the study. This paper takes the neural network for image classification as the research object, summarizes the existing universal perturbation generation algorithm, proposes a universal perturbation generation algorithm combining batch stochastic gradient rise and spherical projection search, achieves loss function reduction through the iterative training of stochastic gradient rise in batch samples, and limits the universal perturbation search to a high-dimensional sphere with radius [Formula: see text] to reduce the search space of universal perturbation. Moreover, the regularized technology is introduced to improve the generation quality of universal perturbations. The experimental results show that compared with the baseline algorithm, the attack success rate increases by more than 10%, the solution efficiency of universal perturbation is improved by one order of magnitude, and the quality controllability of universal perturbation is better.
A Comprehensive Qualitative and Quantitative Review of Current Research in GANs

Generative Adversarial Networks (GANs) are among the most actively researched neural networks in today’s artificial intelligence research. Scientists across different domains, particularly in image processing, constantly utilize variants of GANs to conduct research. The topic has increasingly drawn attention and interest in recent years. Our survey paper reviews the current literature and applications of GANs from both qualitative and quantitative perspectives. This survey also summarizes the challenges and improvement techniques of training GANs. We hope this paper may help researchers interested in GANs and serve as an informative source for ongoing and future work in this field.
More Efficient Construction of Anonymous Signatures

Anonymous signature is a cryptographic tool where the signature of a message can hide its identity of the signer as long as part of the signature is hidden from the verifier. It gives a handy protection of signer identity and is useful in key exchange, auctions and voting.In this paper, we present a new generic construction of anonymous signatures, which is more practical and efficient than all the previous known constructions. For more practicability, we rewrite anonymous signature as two phases, namely signature-commit phase and identity-open phase, and manage to reduce operations in the signature-commit phase where all the participants should proceed, and generate a verification token using the secret key in the identity-open phase which is only required for minority. Moreover, our construction can utilize the structure of the underlying signature, hence it achieves a better efficiency than all the previous ones.As an independent interest, we investigate whether the signatures from the third round finalists of NIST’s PQC standardization are anonymous. Note that they were not designed to be anonymous. Though most of them are not anonymous, interestingly, using the above method we can transform them into anonymous signatures with almost no costs.
New Representations of Modal Functions

In this paper we show how to represent any modal function (i.e. a function expressed by a formula of the propositional modal logic S5) as a tuple of truth-functions, and we provide a nice graphical representation of the modal functions in terms of colorations of edges of certain complete bipartite graphs.
Generating image captions in Polish – experimental study

The image captioning task focuses on generating sentences describing the content of a given image. Recently, some end-to-end solutions based on complex deep neural networks were proposed. However, most of them are producing captions in English, which is a syntactically relatively simple language. Application of captioning methods to more sophisticated languages, e.g., fusional, may cause problems due to the complex syntax. In the paper, we describe the initial results of applying the generative model to produce sentences in Polish, which is the fusional language. To validate the method, we used two datasets based on the Flickr8k database. The first is automatically translated Flickr8k, and the second one is the AIDe dataset consisting of 1000 pictures. The results were validated using BLEU, CIDEr, and BoW metrics.
GLUNet: Global-Local Fusion U-Net for 2D Medical Image Segmentation

Medical image segmentation is a fundamental technology for computer-aided diagnosis and clinical disease monitoring. Most of existing deep learning-based methods solely focus on the region and position of objects without considering edge information which provides accurate contour of objects and is beneficial to medical image segmentation. In this paper, we propose a novel Global-Local fusion UNet model (GLUNet) to address above problem, which contains a Global Attention Module (GAM) and a Local Edge Detection Module (LEDM). In GAM, we embed residual block and convolution block attention module to capture contextual and spatial information of objects. Meanwhile, to obtain accurate edge information of objects in medical image segmentation, we devise the LEDM to integrate edge information into our model. We also propose a multi-task loss function that combines the segmentation loss and the edge loss together to train our GLUNet. Experimental results demonstrate that our proposed method outperforms the original U-Net method and other state-of-the-art methods for lung segmentation in Computed Tomography (CT) images, cell/nuclei segmentation and vessel segmentation in retinal images.
Protected Admittance E-Health Record System Using Blockchain Technology

E-health record is considered as an individual’s health document, which gets shared among several amenities. The EHR system is becoming a popular protagonist as it has the potential to transform the paper-based industry into the digital system for maintaining the patient’s health records. Nevertheless, the E-health record system needs renovation in terms of confidentiality and access to records since the hospital authority is treated as a central holder for the records. The proposed system comes up with a novel solution to modernize the traditional centralized system by the erection of a decentralized framework, whereas the patient is the sole owner of the health document. Implementation of blockchain technology with a decentralized patient-centered structure promotes a secure healthcare system. Blockchain’s open admittance would permit changes to an individual’s her, which needs to be restructured in real-time and makes it instantly available to parties involved for the same. The use of shrewd agreements and dispersed stockpiling improves the therapeutic administrations, including clinical records comparably persistent related proof. This high-level model gives high security and ease of utilizing the highlights through disseminated record where information can’t be held for payment, where every client has a refreshed duplicate of the blockchain.
Smart Engineering Platform: An Overview of New Engineering Taxonomy

This paper presents an engineering taxonomy capable of engineering multi domain systems. Traditional engineering science approaches have succeeded in developing infrastructure, products and applications, but this is not enough to answer the complexity of the issues of living in the 21st century. We develop Smart Engineeering Platform (SEP), having five dimensions (i) hard, (ii) soft, (iii) liquid, (iv) vapour, and (v) the spatial engineering. These engineering dimensions have resulted in design dan implementation of hardware, software, liquidware, vapourware, and spatialware. Combining these dimensions results in multi-domain systems, such as signal processing systems, competitive systems (e.g, product-services-knowledge-value systems PSKV-S), as well as spiritual computing (immersive environments).
Design and Development of Remote Education Model of Ideological and Political Course under Cloud Environment

To solve the problem that traditional ideological and political education is inefficient and students passively accept ideological and political education, this paper designs a remote education platform based on cloud computing, which can make full use of the distributed storage capacity of cloud platform to realize the effective storage and utilization of resources. The scheme combines the powerful data storage capacity of cloud computing platform, and proposes the design method of online learning platform under cloud computing platform. It adopts Eucalyptus, an open source infrastructure cloud service management software, to realize the virtualization design, and uses J2EE to establish the web application of online learning. Finally, the web application is deployed to GAE, and the online learning system based on cloud platform is implemented. Then the performance indicators such as security and reliability of the system are tested to verify the effectiveness and feasibility of the model.
Forecasting Regional Level Solar Power Generation Using Advanced Deep Learning Approach

Reliable integration of solar photovoltaic (PV) power into the electricity grid requires accurate forecasting at the regional level. While previous research has been primarily concerned with forecasting PV power output from a single plant, this research focuses on regional level forecasting which is more beneficial for economic operations of power systems. This paper presents an advanced deep learning-based approach, called CNNs-LSTM Encoder-Decoder (CLED), to predict the regional level aggregated PV power generation for the next day at half-hourly intervals. The proposed approach utilizes the ability of Convolutional Neural Networks (CNNs) to capture and learn the internal representation of intermittent time-series data. It also uses Long Short-Term Memory (LSTM) network for recognizing temporal dependencies in the data. The performance of the CLED model is evaluated using a large data set from the Australian Energy Market Operator (AEMO). Results demonstrate that CLED provides accurate predictions, outperforming baselines and state-of-the-art models in the literature.