Reviewerly/exHarmony huggingface dataset:
Mining of High-Utility Sequence Patterns in Large-Scale Uncertain Databases

In the age of the Internet of Things (IoT), information is collected from sensor devices, which can lead to data loss or data in doubt, among other things. We need to use probability concept to accurately describe the uncertain data we have collected so that we can successfully search a vast undetermined data warehouse for information that can be used in production and applications. As the data in the database may be arranged as a particular order/sequence, whether temporally or spatially, in the field of data processing, the High Utility-Probability Sequential Pattern Mining (HUPSPM) has become an important new research topic and has been widely studied in recent years. The timestamps has led to the development of a considerable number of efficient algorithms for sequential mining. However, these methods have several drawbacks, and most of them cannot handle the very huge datasets. For this reason, the implementation of an advanced MapReduce framework for big data processing is the solution to the problems caused by the currently used approaches. The method presented in this paper has the potential to eliminate the need for multiple database scans, to divide the database into several partitions, and to extend the capabilities of parallel computing. The original database is "pruned" according to the previously defined method that can be used to reduce the total number of potential candidates in a time and resource efficient manner. The approach presented in this paper has been shown in experiments to be very beneficial in finding sequences with high utility and probability in huge datasets.
ConCS: A Continual Classifier System for Continual Learning of Multiple Boolean Problems

Human intelligence can simultaneously process many tasks with the ability to accumulate and reuse knowledge. Recent advances in artificial intelligence, such as transfer, multitask, and layered learning, seek to replicate these abilities. However, humans must specify the task order, which is often difficult particularly with uncertain domain knowledge. This work introduces a continual-learning system (ConCS), such that given an open-ended set of problems once each is solved its solution can contribute to solving further problems. The hypothesis is that the evolutionary computation approach of learning classifier systems (LCSs) can form this system due to its niched, cooperative rules. A collaboration of parallel LCSs identifies sets of patterns linking features to classes that can be reused in related problems automatically. Results from distinct Boolean and integer classification problems, with varying interrelations, show that by combining knowledge from simple problems, complex problems can be solved at increasing scales. 100% accuracy is achieved for the problems tested regardless of the order of task presentation. This includes intractable problems for previous approaches, e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -bit Majority-on. A major contribution is that human guidance is now unnecessary to determine the task learning order. Furthermore, the system automatically generates the curricula for learning the most difficult tasks.
Multi-Scale Self-Calibrated Dual-Attention Lightweight Residual Dense Deraining Network Based on Monogenic Wavelets

Image rain removal is an essential problem of common concern in the fields of image processing and computer vision. Existing methods have resorted to deep learning techniques to separate rain streaks from the background by leveraging some prior knowledge. However, the mismatch between the size of the rain streaks during the training and testing phases, especially when large rain streaks are present, frequently leads to unsatisfactory deraining results. To address this issue, we propose a multi-scale self-calibrated dual attention lightweight residual dense deraining network (MDARNet) for better deraining performance. Specifically, the network consists of monogenic wavelet transform-like hierarchy and self-calibrated dual attention mechanism. With the help of scale-space properties of the monogenic wavelet transform, key features at different scales can be extracted at the same location, which makes it easier to match structural features across scales. The self-calibrated double attention mechanism was used as a basic model for enhancing the channel dependence and spatial correlation between each layer component of the monogenic wavelet transform. Thus, the network can establish long-range dependencies and take advantage of rich contextual information and multi-scale redundancy to accommodate rain streaks of different shapes and sizes. Experiments on synthetic and real image datasets show that the method outperforms many of the latest single-image deraining methods in terms of visual and quantitative metrics. The source code can be obtained from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/smart-hzw/MDARNet</uri> .
Sensor Data Simulation with Wandering Behavior for the Elderly Living Alone

The number of elderly people living alone has been increasing worldwide. The Internet of Things (IoT) technology has a high potential in protecting them from risks such as falls. This can be achieved by continuously monitoring the behavior of the residents in a smart home. To develop anomaly detection algorithms in such smart homes, we need a simulator to produce realistic sensor data over a long period of time. Unfortunately, however, existing simulators lack a high degree of reproducibility.In this study, we developed a novel behavioral simulator centered on wandering behavior. Users can easily and intuitively embed their knowledge in the simulator, keeping the exact definitions of target anomalies. Sensor data can be obtained from several types of sensors attached to the simulated home. We focus on simulating wandering behavior as an important anomaly, considering the degree of progress of dementia as a latent variable. We show that the proposed simulator is more usable than previous simulators from the view point of reproducibility.
A Fuzzy Scheduling Strategy for Online Multi-workflows in IaaS Clouds

With the development of cloud computing, more and more complex workflows have been migrated to clouds and executed with cloud resources. In clouds, it is a a challenging issue to minimize workflow execution cost for scheduling workflow with the deadline constraint. Existing studies are mainly done subject to rigorous conditions without fluctuations, ignoring the fact that workflow scheduling is typically present in uncertain environment. In this paper, we propose a workflow fuzzy scheduling algorithm (WFSA) which aims to minimize workflow scheduling cost and satisfy deadline constraints. In WFSA, we adopt the triangular fuzzy numbers (TFNs) to represent the task processing time and rank tasks to form a scheduling order by the fuzzy sorting strategy. A fuzzy deadline assignment method based on partial critical path (PCPs) of workflows is designed to decomposes the workflow deadline constraint into tasks sub-deadline constraints. A cost-driven fuzzy scheduling strategy is proposed to heuristically select virtual machines (VMs) for minimizing workflow scheduling cost and meeting the task sub-deadline constraint. The experimental results show that our algorithm can effectively reduce the workflow execution cost, compared with other benchmark solutions.
A Novel Particle Swarm Optimizer and Its Application to the Yield Curve Estimation Problem

Particle swarm optimization (PSO) has been considered as one of the main swarm intelligence algorithms for solving single-objective optimization problem. How to update the velocities and positions of a swarm of particles is key to its optimization performance. In this paper, we propose to use the moving average of the local best positions visited so far as a key information to update the velocities and positions. Further, a central learning strategy is proposed in which the center of the local best positions is computed and used to update the global best position. Combining these two strategies with the updating formulas which are inspired by the free electron model in metal conductors placed in an external electric field, we name the proposed PSO algorithm as PSO with moving average and central learning strategies (dubbed as MAPSO). We test the performance of MAPSO on the CEC 2017 test problems with 10 and 30 dimensions. Experimental results show that MAPSO significantly outperforms some well-known PSOs in general. We then apply MAPSO on a very challenging real-world problem, i.e. the yield curve estimation problem, in macroeconomics. Our experimental study on the yield curve estimation problem with Shanghai interbank offered rates shows that MAPSO can effectively solve the problem and achieve the state-of-the-art performance.
An Approach to Classify Thumbnail Images on Video Sites by the Number of Accesses

In recent years, YouTube and other video sites have become wildly popular, and many Internet users spend a lot of time watching them every day. Since popular sites have sponsors, and the amount of income depends on the number of accesses, each channel operator is devising various methods. On the other hand, viewers often choose videos based on the thumbnail images, and the key to gaining access is how to attract them with the thumbnail images. In this paper, we use machine learning to classify thumbnail images on video sites and find the trends of thumbnail images that are expected to attract a lot of traffic. In our experiments, we use YouTube as the video site and Google Teachable Machine as the classifier. As a result, it was found that there is a certain trend between thumbnail images and the number of accesses.
Innovating in the Tourism Industry Through Virtual Reality (VR) and Education in the Hotel Business

In the interdisciplinary co-operation of experts in the fields of technology, hospitality, and pedagogy, a 3D training module was created, which simulates the environment of a hotel reception. Key elements of communication between hotel staff and various types of guests were identified, and variant scenarios of communication situations were prepared. Scenarios are based on the principles of effective simulation. The methodology of training communication skills of hotel employees using virtual reality tools was compiled on the basis of foreign research in the field of using virtual reality in training processes. The tool is a pilot test in teaching tourism workers, specifically for the development of their communication skills in dealing with clients. The paper presents, in detail, a model of virtual reception and methodology of communication skills training, which was verified on students who are preparing to work in tourism, as well as on hotel staff with experience.
Towards Class Interpretable Vision Transformer with Multi-Class-Tokens

For multi-label classification tasks, vision transformers exist two fundamental issues. First, ViT cannot provide a clear per-class saliency region for multi-label classification tasks. This greatly restricts the interpretability of ViT for multi-label classification. Second, when processing images with multiple categories, the single feature vector, i.e., class token, of ViT gathers features of foreground regions from different categories. In this way, the class token indeed interfuses features of multiple objects and could not well distinguish the key feature of multiple objects, thus restricting the network performance. To alleviate these issues, we present a Multi-Class-Tokens-based vision transformer for multi-label image classification. MCT-ViT assigns each class token to a specific category, and generates a corresponding class-level attention map by cross attention module, thus providing a per-class saliency region (distinguishable feature). Since those token patches having high attention scores with a specific class token are the saliency regions corresponding to a certain class, improving the interpretability and also boosting the performance of tasks where detecting and identifying multiple categories are needed. Besides, we use a novel non-parametric scoring method instead of the fully connected classifier in ViT. Specifically, since each class token actually performs binary classification, we can directly compute its $$\ell _2$$ norm to obtain a classification score of the corresponding category for classification. Experimental results on multi-label classification show that our MCT-ViT achieves superior performance over the state-of-the-art on popular benchmark datasets while enjoying per-class interpretability without extra training.
Porting Signal Processing from Undirected to Directed Graphs: Case Study Signal Denoising with Unrolling Networks

Directionality is an essential feature of many real-world networks, but problematic in graph signal processing (GSP) because there is no obvious choice of Fourier basis. In this work we investigate how to port GSP methods from undirected to directed graphs using recent work on graph signal denoising using trainable networks as a case study. We consider five notions of directed Fourier bases from the literature and different approaches for porting, from ad-hoc to conceptual. Our experimental results show that directionality does matter, the importance of a shift operator related to the chosen basis, and which directed Fourier basis may be best suited for applications. The best variant also provides a promising method for denoising signals on directed graphs.
Machine Learning Approach for Predictive Maintenance in Hydroelectric Power Plants

The future of hydropower industry has as key elements, optimization in operation and maintenance, costs reduction and increase of reliability. This means greater challenges in the operation of hydroelectric power plants, therefore, greater demands in maintenance. With technology advances and its role in the industrial sector through the revolution 4.0 or Industry 4.0, artificial intelligence and machine learning applications enables the development and modernization of current maintenance techniques in hydropower plants, through condition monitoring, fault diagnosis and predictive maintenance, thus, an early detection can save a lot of time and money.In this study, two techniques are proposed to enable predictive maintenance in the Peña Blanca hydroelectric power plant, using two deep learning models for anomaly detection. The first one consists of a Deep Neural Network with Logistic Regression to classify various types of failures, for the second one a Recurrent Long Short-Term Memory neural network (LSTM) with Autoencoder is used to classify various flaws. With the first model it was found that it is possible to generalize several types of failures, while the LSTM model adjust better on detecting high temperatures on generator bearings since was a failure that occurred frequently during the study.
Human Aspects in Software Development: A Systematic Mapping Study

Software development is a process that requires a high level of human talent management to ensure its success. This makes it a topic of interest to the software industry and research. Considering this interest, it is evident the need to know the aspects that have been studied, how they have been measured, and what data analysis methods have been used. This paper presents an analysis of the human aspects associated with the software development process, identifying procedures and methods used to analyze data and its measurement. A systematic mapping with a sample of 99 studies identified by their relationship with the proposed topic was used as the research method. The main findings show that one of the most studied is personality. This aspect is related to the performance of software development teams and is a key variable for its conformation. Concerning the most used data source, we find the survey based on self-reporting. Finally, descriptive statistics is the most frequent method of analysis, which is performed prior to other methods such as correlation or regression analysis. The results suggest a wide spectrum of human aspects to be studied in Software Engineering, and interesting potential for analysis by identifying interesting methods other than self-reporting.
Effective Prevention of Semantic Drift in Continual Deep Learning

Lifelong machine learning or continual learning models attempt to learn incrementally by accumulating knowledge across a sequence of tasks. Therefore, these models learn better and faster. They are used in various intelligent systems that have to interact with humans or any dynamic environment. Dynamically expandable networks are continual deep learning models that allow its architecture to expand with a sequence of tasks. The model retains knowledge from the previous tasks that results in high performance on newer tasks. The existing models use Minkowski distance measures to separate nodes of the current network, resulting in higher catastrophic forgetting. These measures are susceptible to high dimensional sparse vectors, resulting in sub-optimum performance. We propose ang-DEN, as a dynamically expanding continual learning architecture that use angular distance metric. It addresses semantic drift through better separation of nodes achieving 97% average accuracy with an improvement of 1.3% across all tasks on MNIST variant datasets.
LiteLSTM Architecture for Deep Recurrent Neural Networks

Long short-term memory (LSTM) is a robust recurrent neural network architecture for learning spatiotemporal sequential data. However, it requires significant computational power for learning and implementing from both software and hardware aspects. This paper proposes a novel LiteLSTM architecture based on reducing the computation components of the LSTM using the weights sharing concept to reduce the overall architecture cost and maintain the architecture performance. The proposed LiteLSTM can be significant for learning big data where time-consumption is crucial such as the security of IoT devices and medical data. Moreover, it helps to reduce the CO2 footprint. The proposed model was evaluated and tested empirically on two different datasets from computer vision and cybersecurity domains.
Collision-free Minimum-time Trajectory Planning for Multiple Vehicles based on ADMM

The paper presents a practical approach for planning trajectories for multiple vehicles where both collision avoidance and minimum travelling time are simultaneously considered. It is first proposed to exploit the mixed-integer programming (MIP) approach to formulate the collision avoidance paradigm, where the linear dynamic models are utilized to derive the linear constraints. Moreover, travelling time of each vehicle is compromised among them and set to be minimized so that all the vehicles can practically reach the expected destinations at the shortest time. Unfortunately, the formulated optimization problem is NP-hard. In order to effectively address it, we propose to employ the alternating direction method of multipliers (ADMM), which can share the computational burdens to distributive optimization solvers. Thus, the proposed method can enable each vehicle to obtain an expected trajectory in a practical time. Convergence of the proposed algorithm is also discussed. To verify effectiveness of our approach, we implemented it in a numerical example, where the obtained results are highly promising.
A hybrid Xception-Ensemble model for the detection of Computer Generated images

Digital images play a vital role in digital communication due to their applications in various domains like games, movies, and medical and legal spheres. Entities fabricate content through computer-generated images, which causes severe adverse consequences. We propose a novel hybrid Xception-Ensemble approach for distinguishing computer-generated images using the depthwise separable convolution of the Xception architecture. We use depthwise separable convolution and the parameters transferred from the pre-trained ImageNet weights to distinguish the features in computer-generated images with ensemble average learning for efficient classification. The accuracy of the proposed system is better than that of state of the art systems on DSTok, Columbia PRCG and Rahmouni datasets.
Correspondence Reweighted Translation Averaging

Translation averaging methods use the consistency of input translation directions to solve for camera translations. However, translation directions obtained using epipolar geometry are error-prone. This paper argues that the improved accuracy of translation averaging should be leveraged to mitigate the errors in the input translation direction estimates. To this end, we introduce weights for individual correspondences which are iteratively refined to yield improved translation directions. In turn, these refined translation directions are averaged to obtain camera translations. This results in an alternating approach to translation averaging. The modularity of our framework allows us to use existing translation averaging methods and improve their results. The efficacy of the scheme is demonstrated by comparing performance with state-of-the-art methods on a number of real-world datasets. We also show that our approach yields reasonably good 3D reconstructions with straightforward triangulation, i.e. without any bundle adjustment iterations.
Ship Detection in SAR Images Based on Adjacent Context Guide Fusion Module and Dense Weighted Skip Connection

Fusing features from different layers is essential to improve the ship target detection ability in the synthetic aperture radar (SAR) images.Mainstream methods usually perform simple addition or concatenation operations on adjacent feature layers without properly merging their semantic and spatial information, whereas the traditional skip connections are unable to explore sufficient information by the same scale.To address these issues, a ship detection network based on adjacent context guide fusion module and dense weighted skip connection (AFDN) in SAR images is proposed: Adjacent context guide fusion module is specially designed to capture the long-range dependencies of high-level features as weights to multiply with low-level features to fuse adjacent features more efficiently.Furthermore, the dual-path enhanced pyramid is constructed to refine and fuse multi-scale features.Finally, a dense weighted skip connection is proposed by weighted fusion of features of all sizes before the decoder to enrich the feature space.Our anchor-free AFDN outputs the spatial density map and clusters to obtain the rotatable bounding box.The experimental results indicate that the method proposed in this paper surpasses previous ship detection methods and achieves high accuracy on SSDD and AIRSARShip-1.0datasets.
SIGN SCHOOL-An interactive website to promote Indian Sign Language

Hand gestures are one of the nonverbal communication modalities used in sign language. It is most typically used by deaf and dumb persons who have hearing or speech issues to communicate with each other or with non-deaf people.Dumb people are frequently denied regular communication with other members of society, and normal people often struggle to understand and communicate with them. These individuals must rely on an interpreter or some form of visual communication to communicate. It is unlikely that an interpreter will be available at all times, and visual communication is often difficult to comprehend. In this paper, we are proposing a solution by which people can learn and practice the Indian sign language easily. We are proposing a YOLOv5 model which is lightweight, fast and is more accurate than other technologies used. We have achieved around 99.8 percent accuracy which is high enough to detect several gestures in the Indian sign Language. This paper also proposes a creation of an interactive website which will allow the users to easily navigate them through their journey to learn the Indian Sign Language.