Reviewerly/exHarmony huggingface dataset:
Enhanced Invertible Encoding for Learned Image Compression

Although deep learning based image compression methods have achieved promising progress these days, the performance of these methods still cannot match the latest compression standard Versatile Video Coding (VVC). Most of the recent developments focus on designing a more accurate and flexible entropy model that can better parameterize the distributions of the latent features. However, few efforts are devoted to structuring a better transformation between the image space and the latent feature space. In this paper, instead of employing previous autoencoder style networks to build this transformation, we propose an enhanced Invertible Encoding Network with invertible neural networks (INNs) to largely mitigate the information loss problem for better compression. Experimental results on the Kodak, CLIC, and Tecnick datasets show that our method outperforms the existing learned image compression methods and compression standards, including VVC (VTM 12.1), especially for high-resolution images. Our source code is available at https://github.com/xyq7/InvCompress.
Automatic Modulation Classification: A Deep Architecture Survey

Automatic modulation classification (AMC), which aims to blindly identify the modulation type of an incoming signal at the receiver in wireless communication systems, is a fundamental signal processing technique in the physical layer to improve the spectrum utilization efficiency. Motivated by deep learning (DL) high-impact success in many informatics domains, including radio signal processing for communications, numerous recent AMC methods exploiting deep networks have been proposed to overcome the existing drawbacks of traditional approaches. DL is capable of learning the underlying characteristics of radio signals effectively for modulation pattern recognition, which in turn improves the modulation classification performance under the presence of channel impairments. In this work, we first provide the fundamental concepts of various architectures, such as neural networks, recurrent neural networks, long short-term memory, and convolutional neural networks as the necessary background. We then convey a comprehensive study of DL for AMC in wireless communications, where technical analysis is deliberated in the perspective of state-of-the-art deep architectures. Remarkably, several sophisticated structures and advanced designs of convolutional neural networks are investigated for different data types of sequential radio signals, spectrum images, and constellation images to deal with various channel impairments. Finally, we discuss some primary research challenges and potential future directions in the area of DL for modulation classification.
Spatiotemporal Inconsistency Learning for DeepFake Video Detection

The rapid development of facial manipulation techniques has aroused public concerns in recent years. Following the success of deep learning, existing methods always formulate DeepFake video detection as a binary classification problem and develop frame-based and video-based solutions. However, little attention has been paid to capturing the spatial-temporal inconsistency in forged videos. To address this issue, we term this task as a Spatial-Temporal Inconsistency Learning (STIL) process and instantiate it into a novel STIL block, which consists of a Spatial Inconsistency Module (SIM), a Temporal Inconsistency Module (TIM), and an Information Supplement Module (ISM). Specifically, we present a novel temporal modeling paradigm in TIM by exploiting the temporal difference over adjacent frames along with both horizontal and vertical directions. And the ISM simultaneously utilizes the spatial information from SIM and temporal information from TIM to establish a more comprehensive spatial-temporal representation. Moreover, our STIL block is flexible and could be plugged into existing 2D CNNs. Extensive experiments and visualizations are presented to demonstrate the effectiveness of our method against the state-of-the-art competitors.
An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network

In recent years, the penetration of solar power at residential and utility levels has progressed exponentially. However, due to its stochastic nature, the prediction of solar global horizontal irradiance (GHI) with higher accuracy is a challenging task; but, vital for grid management: planning, scheduling & balancing. Therefore, this paper proposes an ensemble model using the extended scope of wavelet transform (WT) and bidirectional long short term memory (BiLSTM) deep learning network to forecast 24-h ahead solar GHI. The WT decomposes the input time series data into different finite intrinsic model functions (IMF) to extract the statistical features of input time series. Further, the study reduces the number of IMF series by combining the wavelet decomposed components (D1-D6) series on the basis of comprehensive experimental analysis with an aim to improve the forecasting accuracy. Next, the trained standalone BiLSTM networks are allocated to each IMF sub-series to execute the forecasting. Finally, the forecasted values of each sub-series from BiLSTM networks are reconstructed to deliver the final solar GHI forecast. The study performed monthly solar GHI forecasting for one year dataset using one month moving window mechanism for the location of Ahmedabad, Gujarat, India. For the performance comparison, the naïve predictor as a benchmark model, standalone long short term memory (LSTM), gated recurrent unit (GRU), BiLSTM and two other wavelet-based BiLSTM models are also simulated. From the results, it is observed that the proposed model outperforms other models in terms of root mean square error (RMSE) & mean absolute percentage error (MAPE), coefficient of determination (R2) and forecast skill (FS). The proposed model reduces the monthly average RMSE by range from 26.04-58.89%, 5.17-31.35%, 23.26-56.06% & 21.08-57% in comparison with benchmark, standalone BiLSTM, GRU & LSTM networks respectively. On the other hand, the monthly average MAPE is reduced by range from 9 to 51.18%, 12.59-28.14%, 30.43-59.19% & 26.54-58.92% in comparison to benchmark, standalone BiLSTM, GRU & LSTM respectively. Further, the proposed model obtained the value of R2 equal to 0.94 and forecast skill (%) of 47% with reference to the benchmark model.
Graph-based few-shot learning with transformed feature propagation and optimal class allocation

Graph neural network has shown impressive ability to capture relations among support(labeled) and query(unlabeled) instances in a few-shot task. It is a feasible way that features are extracted using a pre-trained backbone network, and later adjusted in a few-shot scenario with an episodic meta-trained graph network. However, these adjusted features cannot well represent the few-shot data characteristics owing to the feature distribution mis-match caused by the different optimizations between the backbone and the graph network (multi-class pre-train v.s. episodic meta-train). Additionally, learning from the limited support instances fails to depict true data distributions thus cause incorrect class allocation. In this paper, we propose to transform the features extracted by a pre-trained self-supervised feature extractor into a Gaussian-like distribution to reduce the feature distribution mis-match, which significantly benefits the later meta-training of the graph network. To tackle the incorrect class allocation, we propose to leverage support and query instances to estimate class centers by computing an optimal class allocation matrix. Extensive experiments on few-shot benchmarks demonstrate that our graph-based few-shot learning pipeline outperforms baseline by 12%, and surpasses state-of-the-art results by a large margin under both full-supervised and semi-supervised settings.
QoS-Ledger: Smart Contracts and Metaheuristic for Secure Quality-of-Service and Cost-Efficient Scheduling of Medical-Data Processing

Quality-of-service (QoS) is the term used to evaluate the overall performance of a service. In healthcare applications, efficient computation of QoS is one of the mandatory requirements during the processing of medical records through smart measurement methods. Medical services often involve the transmission of demanding information. Thus, there are stringent requirements for secure, intelligent, public-network quality-of-service. This paper contributes to three different aspects. First, we propose a novel metaheuristic approach for medical cost-efficient task schedules, where an intelligent scheduler manages the tasks, such as the rate of service schedule, and lists items utilized by users during the data processing and computation through the fog node. Second, the QoS efficient-computation algorithm, which effectively monitors performance according to the indicator (parameter) with the analysis mechanism of quality-of-experience (QoE), has been developed. Third, a framework of blockchain-distributed technology-enabled QoS (QoS-ledger) computation in healthcare applications is proposed in a permissionless public peer-to-peer (P2P) network, which stores medical processed information in a distributed ledger. We have designed and deployed smart contracts for secure medical-data transmission and processing in serverless peering networks and handled overall node-protected interactions and preserved logs in a blockchain distributed ledger. The simulation result shows that QoS is computed on the blockchain public network with transmission power = average of −10 to −17 dBm, jitter = 34 ms, delay = average of 87 to 95 ms, throughput = 185 bytes, duty cycle = 8%, route of delivery and response back variable. Thus, the proposed QoS-ledger is a potential candidate for the computation of quality-of-service that is not limited to e-healthcare distributed applications.
Novel binary differential evolution algorithm based on Taper-shaped transfer functions for binary optimization problems

In order to efficiently solve the binary optimization problems by using differential evolution (DE), a class of new transfer functions, Taper-shaped transfer function, is firstly proposed by using power functions. Then, the novel binary differential evolution algorithm based on Taper-shaped transfer functions (T-NBDE) is proposed. T-NBDE transforms a real vector representing the individual encoding into a binary vector by using the Taper-shaped transfer function, which is suitable for solving binary optimization problems. For verifying the practicability of Taper-shaped transfer functions and the excellent performance of T-NBDE, T-NBDE is firstly compared with binary DE based on S-shaped, U-shaped and V-shaped transfer functions, respectively. Subsequently, it is compared with the state-of-the-art algorithms for solving the knapsack problem with a single continuous variable (KPC) and the uncapacitated facility location problem (UFLP). The comparison results show that Taper-shaped transfer functions are competitive than existing transfer functions, and T-NBDE is more effective than existing algorithms for solving KPC problem and UFLP problem.
Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decision. The proposed approach is tested and evaluated on four benchmark mammogram image datasets (MIAS, DDSM, INbreast, and BCDR). We present the results of single- and double-dataset evaluations. Only one dataset is used for training and testing in the single-dataset evaluation, whereas two datasets (one for training, and one for testing) are used in the double-dataset evaluation. The experiment results show that the proposed method yields better results on the INbreast dataset in the single-dataset evaluation, whilst better results are obtained on the remaining datasets in the double-dataset evaluation. The proposed approach outperforms other state-of-the-art models on the Mini-MIAS dataset.
HFNet: Hierarchical feedback network with multilevel atrous spatial pyramid pooling for RGB-D saliency detection

Multiscale features have received considerable attention for improving saliency detection. However, existing methods only perform decoding at multiple scales without exploring feature refinement. We propose a hierarchical feedback network (HFNet) with multilevel atrous spatial pyramid pooling (MASPP) to refine multiscale features for RGB-D saliency detection. The improved MASPP for adaptive refinement is applied to hierarchical network modules to obtain multiscale information. Then, the detailed multiscale information is used for decoding based on a channel attention mechanism with joint information guidance, and the output is fed to the next stage through reverse attention. This iterative refinement reuses feature information and predicts more precise saliency maps with detailed information. Experimental results on seven benchmark datasets show the effectiveness of the proposed HFNet, and ablation studies confirm the effectiveness and superiority of applying its different strategies.
Safe deep reinforcement learning-based adaptive control for USV interception mission

This paper aims to develop a safe learning scheme of the USV interception mission. A safe Lyapunov boundary deep deterministic policy gradient (SLDDPG) algorithm is presented for the USV interception mission. The uniformly ultimate bounded (UUB) stability of control systems is analyzed under finite safety constraints. A single neuron proportional adaptive control (SNPAC) is applied to pre-train the deep policy network for speeding up the training process. The proposed method is evaluated by a series of simulations of the USVs interception and tracking mission. Compared with the existing results, our method can fast converge to the feasible solution subject to safety constraints and demonstrate a high performance in stability and safety by virtual-reality experiments.
A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. NLP allows categorizing documents containing different texts. A huge amount of data is generated on social media sites through social media users. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Heat maps are also generated for every model. In the end, future directions are proposed.
MSTA-Net: Forgery Detection by Generating Manipulation Trace Based on Multi-Scale Self-Texture Attention

Lots of Deepfake videos are circulating on the Internet, which not only damages the personal rights of the forged individual, but also pollutes the web environment. What’s worse, it may trigger public opinion and endanger national security. Therefore, it is urgent to fight deep forgery. Most of the current forgery detection algorithms are based on convolutional neural networks to learn the feature differences between forged and real frames from big data. In this paper, from the perspective of image generation, we simulate the forgery process based on image generation and explore possible trace of forgery. We propose a multi-scale self-texture attention Generative Network(MSTA-Net) to track the potential texture trace in image generation process and eliminate the interference of deep forgery post-processing. Firstly, a generator with encoder-decoder is to disassemble images and performed trace generation, then we merge the generated trace image and the original map, which is input into the classifier with Resnet as the backbone. Secondly, the self-texture attention mechanism(STA) is proposed as the skip connection between the encoder and the decoder, which significantly enhances the texture characteristics in the image disassembly process and assists the generation of texture trace. Finally, we propose a loss function called Prob-tuple loss restricted by classification probability to amend the generation of forgery trace directly. To verify the performance of the MSTA-Net, we design different experiments to verify the feasibility and advancement of the method. Experimental results show that the proposed method performs well on deep forged databases represented by FaceForensics++, Celeb-DF, Deeperforensics and DFDC, and some results are reaching the state-of-the-art.
Exploring AI chatbot affordances in the EFL classroom: young learners’ experiences and perspectives

Professionals within the field of language learning have predicted that chatbots would provide new opportunities for the teaching and learning of language. Despite the assumed benefits of utilizing chatbots in language classrooms, such as providing interactional chances or helping to create an anxiety-free atmosphere, little is known about learners’ actual use of chatbots during language classes or how chatbots affect their motivation to learn a language. To address these gaps, this exploratory study aimed to create an inventory of affordances that chatbots provide in the primary English as a foreign language (EFL) classroom and to explore how the affordances affect psychological aspects in language learners, particularly regarding their motivation to learn English through chatbots. Thirty-six Korean primary school learners participated in a 16-week EFL course that utilized customized chatbots. These chatbots were created using Google’s Dialogflow. After the course, individual in-depth interviews were conducted regarding the participants’ experiences and perceptions of the chatbots. Student-chatbot interaction logs produced during the course were also collected to supplement the interview data. Qualitative analysis of the interview transcripts and interaction logs revealed the presence of pedagogical, technological, and social affordances. Depending on the learner, the chatbot affordances were perceived differently; thus, each affordance acted as either an opportunity or a constraint for English language learning. In addition, this study specifically discussed how these chatbot affordances might have affected psychological states in language learners. Future recommendations regarding the use of chatbots in language classrooms were suggested from both pedagogical and technological perspectives.
Security and privacy of internet of medical things: A contemporary review in the age of surveillance, botnets, and adversarial ML

Internet of Medical Things (IoMT) supports traditional healthcare systems by providing enhanced scalability, efficiency, reliability, and accuracy of healthcare services. It enables the development of smart hardware as well as software platforms that operate on the basis of communication systems and the algorithms that process the data collected by the sensors to support decision-making. Although IoMT is involved in large-scale services provisioning in the medical paradigm; however, the resource-constrained nature of these devices makes them vulnerable to immense security and privacy issues. These vulnerabilities are not only disastrous for IoMT but threaten the whole healthcare ecosystem, which can in turn bring human lives in danger. During the past few years, threat vectors against IoMT have been evolved in terms of scalability, complexity, and diversity, which makes it challenging to detect and provide stringent defense solutions against these attacks. In this paper, we classify security and privacy challenges against different IoMT variants based on their actual usage in the healthcare domain. We provide a comprehensive attack taxonomy on the overall IoMT infrastructure comprising different device variants as well as elaborate taxonomies of security protocols to mitigate attacks against different devices, algorithms and describe their strengths and weaknesses. We also outline the security and privacy requirements for the development of novel security solutions for all the attack types against IoMT. Finally, we provide a comprehensive list of current challenges and future research directions that must be considered while developing sustainable security solutions for the IoMT infrastructure.
The Impact of New Human Resource Management Practices on Innovation Performance during the COVID 19 Crisis: A New Perception on Enhancing the Educational Sector

The study aims to investigate the impact of New Human Resource Management Practices (NHRM) on innovation performance mediating by organizational innovation and innovative work behavior in the educational sector during the COVID-19 pandemic. However, experiential proof to determine this relationship is inadequate. Consequently, quantitative statistical tools were implemented in the study. Using data gathered from 450 employees in the Ministry of Education in Amman-Jordan who participated in the online survey the present results indicate that there is a significant positive relationship linking NHRM practices and innovation performance. Additionally, a mediating role of organizational innovation and innovative work behavior was found. Moreover, the results identified that the relation increases more by organizational innovation mediator. Therefore, based on the results, boosting NHRM practices in the Ministry of Education will reinforce managerial implementation, fostering innovative performance. During the COVID 19 pandemic, the cruel challenges obliged the educational sector to use organizational strategies to achieve innovation within the crucial and unpredictable period, which needs practical, swifter technological practices, making it unbeatable, creative, and motivational competitive. Therefore, applying NHRM can present solutions, resolve problems, and enhance innovation among employees during the response time of a crisis. Form, execution and improved environmental sustainability within organizations that rely on permanent innovation are associated with HR practices.