Reviewerly/exHarmony huggingface dataset:
An optimized deep learning model to ensure data integrity and security in IoT based e-commerce block chain application

E-commerce, often known as electronic commerce, is the purchasing and selling of goods over the internet using electronic devices to share data. Banks and other financial institutions are frequently added as third-party platforms to traditional e-commerce platforms. As a result, it raises issues with integrity and cyber security. We suggest a deep learning-based strategy called the Hybrid Interactive Autodidactic School-Based Teaching-Learning Optimization (HIASTLO) algorithm to address these issues. The IoT-based e-commerce blockchain is used to extract and reject the various cyberattacks in the network, and deep learning is utilised to improve the weight and bias of the neural networks. We used a variety of performance indicators, including accuracy, precision, and recall, to identify cyberattacks. We also evaluated how well our work performed when compared to previous BSIoTNET, BCFC, DRNN, DNN-KNN, MOO-FS, LRNN, and HDLM efforts. Furthermore, MudraChain and NormaChain are used to examine the transaction time of our suggested task. The results show that our suggested work performs better than any other methods and offers highly secure internet services.
IWGAN: Anomaly Detection in Airport Based on Improved Wasserstein Generative Adversarial Network

Anomaly detection is an important research topic in the field of artificial intelligence and visual scene understanding. The most significant challenge in real-world anomaly detection problems is the high imbalance of available data (i.e., non-anomalous versus anomalous data). This limits the use of supervised learning methods. Furthermore, the abnormal—and even normal—datasets in the airport field are relatively insufficient, causing them to be difficult to use to train deep neural networks when conducting experiments. Because generative adversarial networks (GANs) are able to effectively learn the latent vector space of all images, the present study adopted a GAN variant with autoencoders to create a hybrid model for detecting anomalies and hazards in the airport environment. The proposed method, which integrates the Wasserstein-GAN (WGAN) and Skip-GANomaly models to distinguish between normal and abnormal images, is called the Improved Wasserstein Skip-Connection GAN (IWGAN). In the experimental stage, we evaluated different hyper-parameters—including the activation function, learning rate, decay rate, training times of discriminator, and method of label smoothing—to identify the optimal combination. The proposed model’s performance was compared with that of existing models, such as U-Net, GAN, WGAN, GANomaly, and Skip-GANomaly. Our experimental results indicate that the proposed model yields exceptional performance.
ALNS: a Python implementation of the adaptive large neighbourhood search metaheuristic

The alns Python package provides a complete implementation of the adaptive large neighbourhood search (ALNS) metaheuristic algorithm (Pisinger & Røpke, 2010;Røpke & Pisinger, 2006).ALNS has quickly become a favourite in the field of operations research for solving difficult combinatorial problems, including the vehicle routing problem and various scheduling problems.Our package has an easy-to-use API and includes various stopping criteria, a large set of acceptance criteria based on Santini et al. ( 2018), and multiple operator selection schemes.Furthermore, it supports many other single-trajectory neighbourhood search algorithms as special cases, including iterated local search (ILS), variable neighbourhood search (VNS), and the greedy randomised adaptive search procedure (GRASP).The package has already been successfully used for research into methodological improvements of ALNS itself (Reijnen et al., 2022), and for the development of a high-quality ALNS metaheuristic solving an industry problem (Wouda et al., 2023).Because of this success, we expect the package to be useful to the wider operations research community.
Light subpath reservoir for interactive ray-traced global illumination

In this paper, we propose a novel approach for sampling indirect illumination based on bidirectional path tracing and world space resampling reservoir. We cache light subpaths with high contribution in a world space grid, allowing for sampling at arbitrary vertices along the eye path. In order to find optimal samples, we continuously update the reservoirs stored in each cell according to the resampling principles. Experiments have shown that our algorithm achieves noticeable noise reduction in scenes lit by indirect light compared with path tracing, at the cost of additional time and memory consumption, while still maintaining interactive frame rates. In some special cases, our method even shows better results than a plain bidirectional path tracer, in terms of both image quality and performance.
Blind modulation classification in multiple input and output<scp>‐</scp>orthogonal frequency division multiplexing using <scp>time‐frequency</scp> analysis and customized convolutional neural network architecture

Abstract MIMO with OFDM is a powerful communication technology in both army and civilian environments. Because there is no prior knowledge of carrier state information or signal overlapping in MIMO‐OFDM systems, outdated probability and feature‐based approaches can be applied. However, these methods cannot easily be used to provide the channel characteristics. Blind modulation categorization is a critical stage in deploying 5G networks. The issue of blind modulation classification for a multiple input and output (MIMO) antenna system using orthogonal frequency division multiplexing (OFDM) is investigated in this research. The independent component analysis (ICA) method has been used to separate the MIMO modulated signal, and the smoothed pseudo Wigner‐Ville distribution (SPWVD) with a Hamming window of 0.2 s has been used to analyze the time‐frequency characteristics of time‐domain modulated signals to get around the challenge of blind attenuation categorization in MIMO‐OFDM arrangements. The recovered time‐frequency characteristics are then transformed into red‐green‐blue (RGB) spectrogram pictures and a perfect‐tweaked customized CNN in which CNN (convolutional neural network) is integrated with LSTM (long short term memory) is used to categorize modulation kinds based on the illustrations of RGB spectrograms. Finally, a module for merging decisions based on majority voting was employed to combine the categorization findings of all receiving antennas. As a result, a customized CNN‐based blind modulation technique outperforms existing works to find the exact modulation type in the MIMO‐OFDM communication.
Reinforced Whale Optimizer for Ground Robotics : A Hybrid Framework

The paper presents a multi-objective optimization technique for the exploration of unknown space. In the context of robotics, exploration employees construction of the surrounding map using sensor information. Conventionally, for space exploration, the optimization is performed utilizing a single optimization technique with a particular objective function. Utilizing a mono objective function with a specific task of optimizing one particular aspect although simplifies and fasten the optimization process, but adversely effects the map accuracy and exploration depth. Realizing this vital aspect, this paper introduces an optimization technique with multi-objective functions which are simultaneously optimized. This not maximizes the search process but also increases the map accuracy. The proposed framework termed as Reinforced Whale Algorithm (RWO), is based on bio-inspired Whale Optimizer. It starts with the initialization of the whale's population, which are referred to as way-points. These way-points are assumed to be constant once they are set in the initial stage/iterations. The next step involves the position update from the non-dominated way-points catered by the robots. The waypoints are optimized by the algorithm. The performance matrices are carefully analyzed through extended simulations mimicking different conditions environment. After determining the isolated benefits of the algorithm, the results efficacy is then demon started by comparing its performance with three contemporary optimization algorithms namely Coordinated Multi-Robot Exploration (CME), and conventional Whale Optimizer (WO), and Arithmetic Optimizer (AO) integrated with CME. Results indicates that the proposed algorithm greatly enhance the optimization process by enhancing the explored area and reducing the search time.
Abdominal Multi-organ Segmentation Using CNN and Transformer

In this paper, we combine the advantages of convolution local correlation and translation invariance in CNN with Transformer's ability to effectively capture long-term dependencies between pixels to produce high-quality pseudo labels. In order to segment images efficiently and quickly, we select nnU-Net [2] as the final segmentation network and use pseudo labels, unlabeled data and labeled data together to train the network, and then we use Generic U-Net [2], the backbone network of nnU-Net, as final prediction network. The mean DSC of the prediction results of our method on validation set of FLARE2022 Challenge [3] is 0.7580.
3D Cross-Pseudo Supervision (3D-CPS): A Semi-supervised nnU-Net Architecture for Abdominal Organ Segmentation

Large curated datasets are necessary, but annotating medical images is a time-consuming, laborious, and expensive process. Therefore, recent supervised methods are focusing on utilizing a large amount of unlabeled data. However, to do so, is a challenging task. To address this problem, we propose a new 3D Cross-Pseudo Supervision (3D-CPS) method, a semi-supervised network architecture based on nnU-Net with the Cross-Pseudo Supervision method. We design a new nnU-Net based preprocessing. In addition, we set the semi-supervised loss weights to expand linearity with each epoch to prevent the model from low-quality pseudo-labels in the early training process. Our proposed method achieves an average dice similarity coefficient (DSC) of 0.881 and an average normalized surface distance (NSD) of 0.913 on the 2022-MICCAI-FLARE validation set (20 cases).
Exploiting the Power of Equality-Generating Dependencies in Ontological Reasoning

Equality-generating dependencies (EGDs) allow to fully exploit the power of existential quantification in ontological reasoning settings modeled via Tuple-Generating Dependencies (TGDs), by enabling value-assignment or forcing the equivalence of fresh symbols. These capabilities are at the core of many common reasoning tasks, including graph traversals, clustering, data matching and data fusion, and many more related real-world scenarios. However, the interplay of TGDs and EGDs is known to lead to undecidability or intractability of query answering in tractable Datalog+/- fragments, like Warded Datalog+/-, for which, in the sole presence of TGDs, query answering is PTIME in data complexity. Restrictions of equality constraints, like separable EGDs, have been studied, but all achieve decidability at the cost of limited expressive power, which makes them unsuitable for the mentioned tasks. This paper introduces the class of "harmless" EGDs, that subsume separable EGDs and allow to model a very broad class of tasks. We contribute a sufficient syntactic condition for testing harmlessness, an undecidable task in general. We argue that in Warded Datalog+/- with harmless EGDs, ontological reasoning is decidable and PTIME. From such theoretical underpinnings, we develop novel chase-based techniques for reasoning with harmless EGDs and present an implementation within the Vadalog system, a state-of-the-art Datalog-based reasoner. We provide full-scale experimental evaluation and comparative analysis.
Nemo

Weak Supervision (WS) techniques allow users to efficiently create large training datasets by programmatically labeling data with heuristic sources of supervision. While the success of WS relies heavily on the provided labeling heuristics, the process of how these heuristics are created in practice has remained under-explored. In this work, we formalize the development process of labeling heuristics as an interactive procedure, built around the existing workflow where users draw ideas from a selected set of development data for designing the heuristic sources. With the formalism, shown in Figure 1, we study two core problems of (1) how to strategically select the development data to guide users in efficiently creating informative heuristics, and (2) how to exploit the information within the development process to contextualize and better learn from the resultant heuristics. Building upon two novel methodologies that effectively tackle the respective problems considered, we present Nemo, an end-to-end interactive system that improves the overall productivity of WS learning pipeline by an average 20% (and up to 47% in one task) compared to the prevailing WS approach.
LiDAR Translation Based on Empirical Approach between Sunny and Foggy for Driving Simulation

Autonomous driving requires perception technologies with robustness and high accuracy. Lots of deep learning algorithms satisfy this requirement. However, they focus on only image processing and few such algorithms are based on LiDAR. A lack of data is the main reason why there exist few deep learning algorithms based on LiDAR. Various translation technologies have been suggested to handle this problem. Though a lack of data occurs more often with LiDAR than with images, these technologies also focus on only image-to-image translation. Translation technology based on LiDAR is required not only for data augmentation but also for driving simulation, which allows algorithms to practice driving as if they were commanding a real vehicle, before doing so in the real world. Among many translations for data augmentation and driving simulation, translation to foggy is the most challenging task. To verify algorithms for driving simulation, generating foggy data is necessary. Also, it leads to a potential solution for data augmentation. Thus, we propose a GAN-based LiDAR translation algorithm between sunny and foggy for driving simulation. It is the first LiDAR translation approach based on an empirical approach. We tested the proposed method on the JARI data set, which was collected with controlling diverse visible distance settings.
Information System Management of Palm Agriculture using Laravel Framework

Digital transformation has had a significant impact on the government sector and industry and the impact of this development in agriculture. Currently, all management is done conventionally and takes a long time. The absence of good management will make it difficult for oil palm farmers to process existing data and make reports related to their income. Because they still use simple equipment. So, to accelerate and facilitate the work of farmers, a tool or website is needed that can have a positive impact on the community. In this case, we developed an application for an agriculture information management system based on the Laravel framework. The implementation stages of this application use a Iterative Incremental with several customized test cases. With this application, it is hoped that it will provide easy access to data and information and easy land management for oil palm farmers.
Combined Bernstein Polynomial Optimal Reciprocal Collision Avoidance Differential Dynamic Programming for Trajectory Replanning and Collision Avoidance for UAM Vehicles

This paper presents an integration of differential dynamic programming (DDP) with the optimal reciprocal collision avoidance (ORCA) algorithm as the basis for a new algorithm, titled combined Bernstein polynomial optimal reciprocal collision avoidance DDP (COBRA DDP), for trajectory replanning and collision avoidance for urban air mobility (UAM) vehicles. State constrained variants of DDP provide the ability to plan trajectories while avoiding obstacles, but these methods require a large increase in computational time per iteration which hinders the overall speed of the algorithm. ORCA utilizes simplified dynamics to recognize potential collisions along a trajectory and provides an optimal velocity for the avoidance of multiple vehicles and obstacles. These velocity commands, however, may not result in a dynamically feasible trajectory for DDP to plan around. As such, a Bernstein polynomial curve that considers the general dynamic constraints of the vehicle is generated to approximate a trajectory based on the velocity commands. COBRA-DDP optimizes this suggested trajectory via unconstrained DDP to provide a dynamically feasible trajectory that provides collision avoidance. This new trajectory can be applied to the vehicle or used to warm-start the state constrained DDP algorithms to decrease computation time. The benefits and effectiveness of the algorithm are demonstrated on a UAM vertical takeoff and landing (VTOL) vehicle simulation with highly nonlinear dynamics.
Metaverse, mental health and museums in post-COVID-19

In this article, I extend the modalities of mental health integration to the metaverse further toward the alternative case of museums. Some countries have modeled health facilitations of psychological nature to museums either physically or virtually. The metaverse as the incorporation of digital and actual bodies poses a further philosophical question of addressing the network of institutional and personal somatic modifications in the post-COVID-19 era.
A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images

Screening programs use mammography as a diagnostic tool for the early detection of breast cancer. Mammogram enhancement is used to increase the local contrast of the mammogram so that the lesions are more visible in the advanced image. For accurate diagnosis in the early stage of breast cancer, the appearance of masses and microcalcification on the mammographic image are two important indicators. The objective of this study was to evaluate the feasibility of the automatic separation of images of breast tissue microcalcifications and also to evaluate its accuracy.The research was carried out by using two techniques of image enhancement and highlighting of breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor filtering method. After determining the clusters of breast tissue microcalcifications, the clusters are classified using the decision tree classification algorithm. Then, for segmentation, samples suspected of microcalcification are highlighted and masked, and in the last stage, tissue characteristics are extracted. Subsequently, with the help of an artificial neural network (ANN), determining the benign and malignant types of segmented ROI clusters was accomplished. The proposed system is trained with a Digital Database for Screening Mammography (DDSM) developed by the University of South Florida, USA, and the simulations are performed under MATLAB software and the results are compared with previous work.The results of this training performed under this work show an accuracy of 93% and an improvement of sensitivity above 95%.The result indicates that the proposed approach can be applied to ensure breast cancer diagnosis.
Explaining Recommendations through Conversations: Dialog Model and the Effects of Interface Type and Degree of Interactivity

Explaining system-generated recommendations based on user reviews can foster users’ understanding and assessment of the recommended items and the recommender system (RS) as a whole. While up to now explanations have mostly been static, shown in a single presentation unit, some interactive explanatory approaches have emerged in explainable artificial intelligence (XAI), making it easier for users to examine system decisions and to explore arguments according to their information needs. However, little is known about how interactive interfaces should be conceptualized and designed to meet the explanatory aims of transparency, effectiveness, and trust in RS. Thus, we investigate the potential of interactive, conversational explanations in review-based RS and propose an explanation approach inspired by dialog models and formal argument structures. In particular, we investigate users’ perception of two different interface types for presenting explanations, a graphical user interface (GUI)-based dialog consisting of a sequence of explanatory steps, and a chatbot-like natural-language interface. Since providing explanations by means of natural language conversation is a novel approach, there is a lack of understanding how users would formulate their questions with a corresponding lack of datasets. We thus propose an intent model for explanatory queries and describe the development of ConvEx-DS, a dataset containing intent annotations of 1,806 user questions in the domain of hotels, that can be used to to train intent detection methods as part of the development of conversational agents for explainable RS. We validate the model by measuring user-perceived helpfulness of answers given based on the implemented intent detection. Finally, we report on a user study investigating users’ evaluation of the two types of interactive explanations proposed (GUI and chatbot), and to test the effect of varying degrees of interactivity that result in greater or lesser access to explanatory information. By using Structural Equation Modeling, we reveal details on the relationships between the perceived quality of an explanation and the explanatory objectives of transparency, trust, and effectiveness. Our results show that providing interactive options for scrutinizing explanatory arguments has a significant positive influence on the evaluation by users (compared to low interactive alternatives). Results also suggest that user characteristics such as decision-making style may have a significant influence on the evaluation of different types of interactive explanation interfaces.
Joint learning with diverse knowledge for re-identification

Re-identification (re-ID) aims to search the target images of pedestrians or vehicles with the same identity in the non-overlapping camera network. Some factors, such as, cluttered background, different illumination and occlusion, which increases the difficulty of the re-identification task. To fulfill the re-ID task, this paper presents a re-ID method with diverse knowledge, which contains a multi-branch structure to extract various detailed features of samples from multiple perspectives. Furthermore, the attention mechanism is introduced to suppress the influence of background information. Moreover, to improve the discriminative ability, a joint learning strategy is presented to refine the network and align the output of multiple branches. To verify the effectiveness of the proposed method, corresponding experiments have been conducted on multiple public datasets and experimental results demonstrate that our method is effective and achieves the competitive results with existing methods on CUHK03 and Market1501.
Development of an open-source software for isomer enumeration

This article documents enu, a freely-downloadable, open-source and stand-alone program written in C++ for the enumeration of the constitutional isomers and stereoisomers of a molecular formula. The program relies on graph theory to enumerate all the constitutional isomers of a given formula on the basis of their canonical adjacency matrix. The stereoisomers of a given constitutional isomer are enumerated as well, on the basis of the automorphism group of this matrix. The isomer list is then reported in the form of canonical SMILES strings within files in XML format. The specification of the molecule family of interest is very flexible and the code is optimized for computational efficiency. The algorithms and implementations underlying enu are described, and simple illustrative applications are presented. The enu code is freely available on GitHub at https://github.com/csms-ethz/CombiFF .