Reviewerly/exHarmony huggingface dataset:
Seq2seq and Legacy techniques enabled Chatbot with Voice assistance

Increasing usage of gadgets is what can be seen in present days. Gadgets that tend to reduce human effort and anxiety are used the most. The excessive use of these being in contact has made the world lose its eyes or fall under stress. Thus, the voice assistant application is built, which takes up real voice recognition, processes the requirements according to the client, and responds well. An algorithm named seq2seq is used to accomplish the same. Since it has the feature of completing tasks without eye contact, it diminishes the requirement of continually looking at their screen. The latter task is accomplished using verbally ordering the gadgets to do the respective task. The task accomplished by the voice assistant is having a friendly chat and querying the results. Querying the results include searching through Google and summarizing the term. An additional feature added to it is a blink option as we call to alert the user for every n second as prescribed by the user.
Secure Software Development: Infuse Cyber Security to Mitigate Attacks in an Organization

With the emergence of the issue of “cybersecurity” in the industry and the ever-growing importance of software development for innovative fields (IoT, Industry, new mobility, etc.), the researcher wanted to concentrate on a single guide with all acceptable practices in secure software development for the industry. Our customers’ feedback initiated this idea often face the choice of reference to apply and the relevance of the requirements themselves. This research is therefore intended for software architects and developers of industrial applications. It is necessary to pass it through technical and organizational measures to develop software securely. The measures and technical solutions are now numerous and continue to improve day by day. They help make life easier for developers to better prepare a deployment by improving and strengthening the verification and validation phases or exploiting a range of robust security features and mechanisms. Organizational measures will aim to create a positive and dynamic change within development teams and strengthen the product code’s quality control process to improve software security level gradually. These measures include training in secure development and maintenance of the specific skills required to implement technical measures. This research aims to provide a guide to utilizing these measures, combined with the realistic acceptance criteria that suit industrial feedback.
Global Temporal Difference Network for Action Recognition

Temporal modeling still remains as a challenge for action recognition. Most existing temporal models focus on learning local variation between neighbor frames. There exists obvious deviations between local and global variations, such as subtle and notable motion variations. In this paper, we propose a global temporal difference module for action recognition, which consists of two sub-modules, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , a global aggregation module and a global difference module. These two sub-modules cooperate following the idea of using prior knowledge from the global view ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , global motion variation) to guide local learning at each moment. In the global aggregation module, the global prior knowledge is learned by aggregating the visual feature sequence of video into a global vector. In the global difference module, we prepare the difference vector sequence of video by subtracting each local vector from the global vector. Our method performs as a contextual guidance with a global view. The sequential dependency between these difference vectors is exploited with a channel-wise self-attention operation. Finally, the difference vectors at each timestamp are further used to enhance the semantics of the original local features. The enhanced features endow the action recognition has less deviation to understand the variation in the video globally. We instantiate the global temporal difference module into the ResNet block to form a global temporal difference network (GTDNet). Exhaustive experiments are conducted and our method achieves competitive performance at small FLOPs on Something-Something V1 & V2 and Kinetics-400.
Distance-Driven Curve-Thinning on the Face-Centered Cubic Grid

Centerline (curve skeleton) is a frequently used skeleton-like shape descriptor for 3D tubular objects. This paper proposes an endpoint-based sequential curve-thinning algorithm (i.e., an iterative object reduction technique to obtain the centerline) for binary objects sampled on the face-centered cubic (FCC) grid. In order to ensure the centeredness of the resulting centerline, the thinning process is driven by distance transform. The introduced method is evaluated on distance maps computed with various distance definitions (i.e., chamfer or Euclidean distance). To the best of our knowledge, the reported algorithm is the very first curve-thinning method for the FCC grid.
Spatio-Temporal Detail Information Retrieval for Compressed Video Quality Enhancement

The past few years have witnessed the great success of multi-frame quality enhancement for compressed video. Although the existing methods based on deformable alignment have achieved the state-of-the-art performance, they do not pay enough attention to the recovery of detail information. In this work, we propose a Spatio-Temporal Detail Retrieval (STDR) method to promote the recovery of detail information. To alleviate the problem of inaccurate deformable offsets caused by the fixed receptive field, motivated by multi-task learning, we design a plug-and-play Multi-path Deformable Alignment (MDA) module to generate more accurate offsets by integrating the alignment features of different receptive fields, so that the temporal detail information can be better recovered. For the spatial detail information restoration, several residual dense blocks with channel attention layer are utilized in the reconstruction module to explore valuable high-frequency spatial information from the fused multi-path alignment features. Meanwhile, a complementary loss function based on the Pearson correlation coefficient is developed to ameliorate the over-smoothing shortcoming caused by pixel-wise mean square or absolute value loss. Experimental results demonstrate that the proposed STDR network achieves superior performance compared with the state-of-the-art methods in both quantitative and qualitative evaluations.
Reinforcement Learning Based Path Planning Method for Underactuated AUV with Sonar Constraint

Path planning for Autonomous Underwater Vehicle(AUV) is challenging for the reason that the dynamic system of AUV is under-actuated and the main sensor called sonar suffers from a limit detection range. This paper proposed a reinforcement learning (RL) based path planning method for the high speed AUV considering multi constrains. Firstly, an actor-critic based RL algorithm is designed including the constrains of sonar's detection distance, detect angle and AUV's angular rate. Secondly, an improved reward function is proposed considering total path length, angle change and the position of obstacles and target area. Finally, a priority experience replay(PER) technology is applied to RL algorithm to speed the training process. Simulation results show that the improved algorithm plan the reasonable path with mentioned constrains. Compared with conventional method in simulation, the improved algorithm finishes the path planning task with faster convergence speed, shorter path length and smaller cost about angle action.
Blockchain Based Secure Communication for IoT Devices in Smart Cities

Many approaches have been used to secure the Internet of Things (IoT) deployments or applications in recent years. These approaches enable the security system in complex IoT devices or applications, which is the basis of applying practical security algorithms to detect device threats, related patterns, or vulnerabilities. Day by day, the small computing devices have made many technological improvements at an advanced level, which implements over the Internet, named IoT devices. The blockchain has been recently introduced as an alternative solution to complex and challenging security devices or applications. It prefers to security area due to its distributed structure and the cryptographic hash algorithm. These issues arise from the restriction of hardware used in devices. One of the leading security issues is that the attackers now quickly attack/hack our passwords. We use the encryption method to encrypt the communication between nodes to avoid this risk, creating an authentic system. We use a system to secure passwords/transactions from the attacker. This system secures the password through Blockchain technology by generating hash passwords and ciphers that encrypt the original password.
Weather Classification for Multi-class Weather Image Based on CNN

Multiple weather image classification is a very important topic in real life. Convolutional Neural Network (CNN) is a feedforward neural network that excels in image processing, but the accuracy obtained by weather image classification using simple CNN models is not very satisfactory in the previous studies. In machine learning, Support Vector Machine (SVM) is a very powerful classifier. This work proposes an effective classification method by combining CNN and SVM by taking advantage of their respective advantages. At the same time in the weather scene, the brightness of the image is also a point of concern. Hue, Saturation, Value (HSV) color model can visualize the brightness of the image, so the paper experiments on both RGB and HSV images to find which pattern of color space can achieve better result. In the experimental results, the best results are obtained by using a combination of CNN and SVM to analyze RGB images, which can achieve 77.38% in the testing dataset.
A New Optimization Method Based on the Lotka-Volterra System Equations

This new method proposes the interaction that exists in nature between plants through a network of fungi, in nature there is a universe that we cannot see, where there is a frenetic exchange of information and resources between these two living beings such as plants and fungi, between 450 and 480 million years ago began the colonization of plants in the terrestrial environment and this would not have been carried out but through the symbiosis between plants and fungi over the centuries. In this research, we propose to model an optimization method for this type of interaction between plants and fungi by means of the Lotka-Volterra system of equations, 36 mathematical functions will be used to evaluate its performance and, in another study, we will evaluate it against other methods to check its effectiveness. The Lotka-Volterra system of equations offers us to model the different types of relationships that we find between these two living entities that we propose in this study.
A Location-Aware Verifiable Outsourcing Data Aggregation in Multiblockchains

With the rapid development of the Internet of Vehicles (IoV), location-aware outsourcing data aggregation is evolving into a fundamentally key role for analyzing a significant amount of data among smart devices. Due to the spatial property of the data in IoV, location privacy and data security in outsourcing data aggregation face critical challenges. Meanwhile, because of the diversity of entities in IoV, how aggregating data from multiple domains is also a serious issue. In this article, we propose a location-aware verifiable outsourcing data aggregation (LAVODA) for IoV where aggregators are hierarchical for the cross-chain mechanism in multiblockchains. With homomorphic encryption and homomorphic commitment, we achieve the verifiability of the aggregation while ensuring data confidentiality. Specifically, we combine twin-DH with circle-based location verification to ensure the privacy of the requester’s location strategy and data providers’ locations. The security analysis shows that our scheme can achieve the above secure properties. In addition, the experimental results demonstrate that our scheme is efficient and feasible in practice.
Robust Tracking via Uncertainty-Aware Semantic Consistency

Robust tracking has a variety of practical applications. Despite many years of progress, it is still a difficult problem due to enormous uncertainties in real-world scenes. To address this issue, we propose a robust anchor-free based tracking model with uncertainty estimation. Within the model, a new data-driven uncertainty estimation strategy is proposed to generate uncertainty-aware features with promising discriminative and descriptive power. Then, a simple yet effective pyramid-wise cross correlation operation is constructed to extract multi-scale semantic features that provide rich correlation information for uncertainty-aware estimation and thus enhances the tracking robustness. Finally, a semantic consistency checking branch is designed to further estimate uncertainty of output results from the classification and regression branches by adaptively generating semantically consistent labels. Experiments on six benchmarks (i.e., OTB100, VOT2018, VOT2020, TrackingNet, GOT-10K and LaSOT) show the competing performance of our tracker with 130 FPS.
Sketch2Process: End-to-end BPMN Sketch Recognition Based on Neural Networks

Process models play an important role in various software engineering contexts. Among others, they are used to capture business-related requirements and provide the basis for the development of process-oriented applications in low-code/no-code settings. To support modelers in creating, checking, and maintaining process models, dedicated tools are available. While these tools are generally considered as indispensable to capture process models for their later use, the initial version of a process model is often sketched on a whiteboard or a piece of paper. This has been found to have great advantages, especially with respect to communication and collaboration. It, however, also creates the need to subsequently transform the model sketch into a digital counterpart that can be further processed by modeling and analysis tools. Therefore, to automate this task, various so-called sketch recognition approaches have been defined in the past. Yet, these existing approaches are too limited for use in practice, since they, for instance, require sketches to be created on a digital device or do not address the recognition of edges or textual labels. Against this background, we use this paper to introduce Sketch2Process, the first end-to-end sketch recognition approach for process models captured using BPMN. Sketch2Process uses a neural network-based architecture to recognize the shapes, edges, and textual labels of highly expressive process models, covering 25 types of BPMN elements. To train and evaluate our approach, we created a dataset consisting of 704 hand-drawn and manually annotated BPMN models. Our experiments demonstrate that our approach is highly accurate and consistently outperforms the state of the art.
Face Recognition via Multi-Level 3D-GAN Colorization

Rapid development in sketch-to-image translation methods boosts the investigation procedure in law enforcement agencies.But, the large modality gap between manually generated sketches makes this task challenging.Generative adversarial network (GAN) and encoder-decoder approach are usually incorporated to accomplish sketchto-image generation with promising results.This paper targets the sketch-to-image translation with heterogeneous face angles and lighting effects using a multi-level conditional generative adversarial network.The proposed multilevel cGAN work in four different phases.Three independent cGANs' networks are incorporated separately into each stage, followed by a CNN classifier.The Adam stochastic gradient descent mechanism was used for training with a learning rate of 0.0002 and momentum estimates β1 and β2 as 0.5 and 0.999, respectively.The multi-level 3Dconvolutional architecture help to preserve spatial facial attributes and pixel-level details.The 3D convolution and deconvolution guide the G1, G2 and G3 to use additional features and attributes for encoding and decoding.This helps to preserve the direction, postures of targeted image attributes and special relationships among the whole image's features.The proposed framework process the 3D-Convolution and 3D-Deconvolution using vectorization.This process takes the same time as 2D convolution but extracts more features and facial attributes.We used pre-trained ResNet-50, ResNet-101, and Mobile-Net to classify generated high-resolution images from sketches.We have also developed, and state-of-the-art Pakistani Politicians Face-sketch Dataset (PPFD) for experimental purposes.Result reveals that the proposed cGAN model's framework outperforms with respect to Accuracy, Structural similarity index measure (SSIM), Signal to noise ratio (SNR), and Peak signal-to-noise ratio (PSNR).
Context-Aware and User Adaptive Smart Home Ecosystems Using Wearable and Semantic Technologies During and Post COVID-19 Pandemic

In recent years, technology-based health programs, including context-aware and user-adaptive smart home ecosystems, have shown tremendous potential to limit disparities in healthcare access and improve uptake in health programs, thereby improving health outcomes. In this chapter, we review the potential use and applications of context-aware and user-adaptive smart home ecosystems to support the diagnosis, monitoring and management of health conditions. Further, we also discuss the barriers and limitations of using wearable and smart home technologies, their facilitators, current gaps and future opportunities. The chapter provides evidence-based applications and a comprehensive understanding of the use of wearables and smart home ecosystems during and after COVID-19 pandemic for health care providers, researchers, students, and technology developers.
Formal Verification of Saber’s Public-Key Encryption Scheme in EasyCrypt

In this work, we consider the formal verification of the public-key encryption scheme of Saber, one of the selected few post-quantum cipher suites currently considered for potential standardization. We formally verify this public-key encryption scheme's security and $$\delta $$ -correctness properties, i.e., the properties required to transform the public-key encryption scheme into an secure and $$\delta $$ -correct key encapsulation mechanism, in EasyCrypt. To this end, we initially devise hand-written proofs for these properties that are significantly more detailed and meticulous than the presently existing proofs. Subsequently, these hand-written proofs serve as a guideline for the formal verification. The results of this endeavor comprise hand-written and computer-verified proofs which demonstrate that Saber's public-key encryption scheme indeed satisfies the desired security and correctness properties.
Explainability in Mechanism Design: Recent Advances and the Road Ahead

Designing and implementing explainable systems is seen as the next step towards increasing user trust in, acceptance of and reliance on Artificial Intelligence (AI) systems. While explaining choices made by black-box algorithms such as machine learning and deep learning has occupied most of the limelight, systems that attempt to explain decisions (even simple ones) in the context of social choice are steadily catching up. In this paper, we provide a comprehensive survey of explainability in mechanism design, a domain characterized by economically motivated agents and often having no single choice that maximizes all individual utility functions. We discuss the main properties and goals of explainability in mechanism design, distinguishing them from those of Explainable AI in general. This discussion is followed by a thorough review of the challenges one may face when working on Explainable Mechanism Design and propose a few solution concepts to those.
Decentralisation Conscious Players and System Reliability

We propose a game-theoretic model of the reliability of decentralised systems based on Varian’s model of system reliability [28], to which we add a new normalised total effort case that models decentralisation conscious players who prioritise decentralisation. We derive the Nash equilibria in the normalised total effort game. In these equilibria, either one or two values are played by players that do not free ride. The speed at which players can adjust their contributions can determine how an equilibrium is reached and equilibrium values. The behaviour of decentralisation conscious players is robust to deviations by other players. Our results highlight the role that decentralisation conscious players can play in maintaining decentralisation. They also highlight, however, that by supporting an equilibrium that requires an important contribution they cannot be expected to increase decentralisation as contributing the equilibrium value may still imply a loss for many players. We also discuss practical constraints on decentralisation in the context of our model.
Asymptotic Stability in Reservoir Computing

Reservoir Computing is a class of Recurrent Neural Networks with internal weights fixed at random. Stability relates to the sensitivity of the network state to perturbations. It is an important property in Reservoir Computing as it directly impacts performance. In practice, it is desirable to stay in a stable regime, where the effect of perturbations does not explode exponentially, but also close to the chaotic frontier where reservoir dynamics are rich. Open questions remain today regarding input regularization and discontinuous activation functions. In this work, we use the recurrent kernel limit to draw new insights on stability in reservoir computing. This limit corresponds to large reservoir sizes, and it already becomes relevant for reservoirs with a few hundred neurons. We obtain a quantitative characterization of the frontier between stability and chaos, which can greatly benefit hyperparameter tuning. In a broader sense, our results contribute to understanding the complex dynamics of Recurrent Neural Networks.
A new face image manipulation reveal scheme based on face detection and image watermarking

Face image manipulation (FIM) algorithms and applications are increasing and distributing rapidly. Nowadays, one can easily find an application to manipulate face images for different purposes. After the development and popularity of “DeepFakes”, the research community highlighted the necessity of implementing new detection techniques that can reveal FIM. The available FIM detection techniques have different limitations and most of the presented schemes require a prior knowledge of the FIM method that has been applied to generate the manipulated face image which makes them restricted. In this paper, we present a new face image manipulation reveal (FIMR) scheme based on face detection algorithm and image watermarking technique in the transform domain. The proposed scheme does not need a prior knowledge of the FIM method thus it can reveal different types of FIM. Experiments have been conducted to evaluate the performance of the proposed FIMR scheme for face images with different sizes. The results proved that the proposed scheme can successfully reveal different manipulation attacks such as face swap, expression swap, attribute attacks, retouching attacks, and morphing attacks.