Reviewerly/exHarmony huggingface dataset:
CaMEL: Case Marker Extraction without Labels

We introduce CaMEL (Case Marker Extraction without Labels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages.We propose a first model for CaMEL that uses a massively multilingual corpus to extract case markers in 83 languages based only on a noun phrase chunker and an alignment system.To evaluate CaMEL, we automatically construct a silver standard from UniMorph.The case markers extracted by our model can be used to detect and visualise similarities and differences between the case systems of different languages as well as to annotate fine-grained deep cases in languages in which they are not overtly marked.
LA-HDR: Light Adaptive HDR Reconstruction Framework for Single LDR Image Considering Varied Light Conditions

The high dynamic range (HDR) image recovery from the low dynamic range (LDR) image aims to estimate HDR image by decompressing luminance range and enhancing details of the LDR input. In practical usages, when faced with the over-exposed, the under-exposed or the low-light images, the state-of-art prediction methods lack the capability for ideally handling them. Aiming for this, a light adaptation HDR recovery framework (LA-HDR) is proposed, which includes the multi-images generation for adaptive details amplification in different light ranges, and the following multi-details fusion. To create the multi-images, first, the designed bit-depth enhancement network ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EnhanceNet</i> ) produces the high bit-depth result with enhanced contrast. This result can be furtherly processed by user-defined denoising method to refrain the low-light noise. Meanwhile, the proposed exposure bias network ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EBNet</i> ) estimates the global exposure bias of the input for rectifying the mid-range details. With the enhanced result and the exposure bias, the designed transfer functions adaptively create three multi-images containing the enhanced details in different light ranges, and they are fused by the designed multi-images fusion network ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FuseNet</i> ) for the final HDR prediction. The amplification and fusion scheme ensures robust HDR recovery under different light conditions, eliminating high-light recovery artifacts from previous methods. The proposed fusion masks generation (FMG) and the global feature embedding (GFE) modules in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FuseNet</i> help eliminate the fusion artifacts. Experimental results show that LA-HDR acquires the best average performance under various light conditions, and it receives low influence from the input light conditions among the tested state-of-art HDR recovery methods.
Multiauthority Attribute-Based Encryption With Dynamic Membership From Lattices

Attribute-based encryption is useful for one-to-many encrypted message sending.However, most attribute-based encryption schemes authorize and issue attributes to users by a single authority.Such a scenario conflicts with practical requirements and may cause the key-escrow problem.Hence, decentralization of authority is a critical issue in attribute-based encryption.Besides, dynamic membership management is another important issue in attribute-based encryption.With dynamic membership management, a system may update the user's attributes without affecting other users, making the system more flexible and practical.On the other hand, with the rapid development of quantum computers nowadays, encryption schemes based on traditional mathematical problems are at the risk of quantum attacks.Among the existing quantumresistant mathematical architectures, lattice-based cryptography is the most widely studied.Thus, we propose a multi-authority attribute-based encryption scheme with dynamic membership from lattices to solve the above problems.Moreover, we also formally prove the security of the proposed scheme under the decisional learning with errors assumption.
A Systematic Literature Review of Machine Learning Applications in Software Engineering

Machine Learning (ML) has been a concern in Software Engineering (SE) over the past years. However, how to use ML and what it can offer for SE is still subject to debate among researchers. This paper investigates the application of ML in SE. The goal is to identify the used algorithms, the addressed topics and the main findings. It performs a Systematic Literature Review (SLR) of peer-reviewed studies published between 1995 and 2020. Data extracted from the studies show that ML algorithms are of great practical value in the different activities of software development process, especially “Software specification” and “Software validation” since “Software bug prediction” and “Software quality improvement” are the most recurring research topics.
An Integrated Data-Driven Surrogate Model and Thermofluid Network-Based Model of a 620 Mwe Utility-Scale Boiler Using a Fully Connected Mixture Density Network

An integrated data-driven surrogate model and one-dimensional (1D) process model of a 620 MW e utility scale boiler is presented. A robust and computationally inexpensive computational fluid dynamic (CFD) model of the utility boiler was utilized to generate the solution dataset for surrogate model training and testing. Both a standard multi-layer perception (MLP) and MLP connected mixture density network (MDN) machine learning architectures are compared for use as a surrogate model to predict the furnace heat loads and the flue-gas inlet conditions to the convective pass. A hyperparameter search was performed to find the best MLP and MLP-MDN architecture. The MDN was selected for surrogate model integration since it showed comparable accuracy and provides the ability to predict the uncertainty in the results. Validation of the integrated model against plant data was performed for a wide range of loads and critical results were predicted within 5-8% of the measured results. The validated model was subsequently used to investigate the effects of using a poor-quality fuel for the 100% maximum continuous rating (MCR) load case. The uncertainties predicted by the surrogate model were propagated through the integrated model using the Monte Carlo technique, adding valuable insight into the operational limits of the power plant and the uncertainties associated with it.
Assessing the Reusability of Design Principles in the Realm of Conversational Agents

Conversational Agents (CAs) provide the means to foster user experience design through seizing their interaction capability, knowledgeability, and human-like behavior. To support practice and academia in designing CAs, IS researchers have been creating design knowledge in the form of design principles (DPs) guided by the Design Science paradigm. However, scientific literature in this vein is dispersed and lacks an axis of cohesion and transferability to sustained practice usage. This raises the question of reusability of design principles in the realm of CAs. Therefore, in this study, we conduct a Systematic Literature Review to retrieve and assess design principles of existing design science papers dealing with CAs with regard to their reusability. Our findings indicate that the Design Science community, in our case in the domain of CAs, seems to face challenges in creating reusable design principles. We discuss this observation and provide avenues on how to move forward.
Automated Coverage Path Planning for Inspection of Panels Using Camera Viewpoints

The coverage of a surface using multiple view points is a topic of great interest for robotic path planning in inspection applications. Two approaches for coverage path planning are broadly addressed in the literature - geometric methods and optimization methods. Optimization methods frame the problem in terms of NP-hard problems such as traveling view planning, art gallery problem etc. While the optimization methods may be the most flexible, they are frequently difficult to implement in practical applications due to their NP hard nature. Geometric methods, while being less flexible, are more suitable for specific problems, and are fast and easy to implement. We present here a geometric algorithm for the coverage path planning of panels used for Aerospace applications using a generic camera model which can represent area inspection techniques like thermography and laser shearography. This algorithm relies on drawing a 2-D grid on the 3-D surface of the panel using geodesic lines on the surface. We show that the proposed algorithm has good results for real world scenarios involving the inspection of such panels, and emphasize its speed, simplicity and reliability for such applications.
State of art on: Features extraction, recognition and detection of currency notes

Currency has become absolutely necessary part in human life. At present recognition of counterfeit and real currency notes is very important. If the application learns to classify the denominations of the currency and detect the counterfeit currency then it will improve the accuracy of the recognition and confidence of the people. Implementation of deep learning techniques on various applications is increasing rapidly, and now many researchers are using deep learning techniques for the applications related to currency notes. In this paper various techniques which have been used for extracting the features from currency note, recognizing the currency note and detecting the counterfeit note have been presented.
Accelerating Graph Processing With Lightweight Learning-Based Data Reordering

Graph processing is a vital component in various application domains. However, a good graph processing performance is hard to achieve due to its intensive irregular data accesses. Noticing that in real-world graphs, a small portion of vertices occupy most connections, several techniques are proposed to reorder vertices based on their access frequency for better data access locality. However, these approaches can be further improved by identifying reordered data more effectively, which will reduce reordering overhead and improve overall performance. In this letter, we propose <i>Learning-Based Reordering (LBR)</i>, a novel lightweight framework that identifies and reorders hot data adaptively for given graphs, algorithms, and threads. Our experimental evaluation indicates that <i>LBR</i> decreases reordering overhead by 24.7&#x0025; while improves performance by 9.9&#x0025; compared to the best-performing existing scheme.
Identification of Facial Expressions Using Local Phase Quantization

Facial Expression takes non-verbal signals, shows a major part in social associations. The Face recognition arrangement remains route of finding expressive formal of a person. In this arrangement taken picture is related with accomplished data set available in data base and expressive formal of appearance will be presented. On behalf scheming a solid facial descriptor, we apply the Local Phase Quantization. It is a modest identical capable texture hand which makes the pixel of appearance by look back the quarter of individually pixel and reflects the outcome as a twin number. Histogram will be formed by using the operator label of Local Binary Patterns. The gratitude shows the planned technique drive be assessed by using the trained database.
Building up Personas by Clustering Behavior Motivation from Extreme Users

This paper introduces a new practical approach to developing a persona via the extreme user. The behavior of extreme users provides the distinguished dimension on grouping different persona. This research collects data from observation and deep-in interviews, with analyzed the interview data sentence by sentence, five dimensions are filtered by the principles that can provide guidance for the design and distinguish the user portrait. The scales on each dimension are used to describe the refine characters of the persona, to have the persona be stereo and vivid. Finally, five personas were set up in the theme of health in the middle class: “corporation slave sport by mind”, “self-discipline middle in a dilemma”, “sports talent overdo lead to injury”, “home care keeper” and “ageless lady”. Common trends are also understood thoroughly from interview data to support persona. This approach is trying to grab sensible information from typical users to build up a persona and to get precise user features for commercial use.
FETTrans: Analysis of Compound Interference Identification Based on Bidirectional Dynamic Feature Adaptation of Improved Transformer

With the development of 5G network construction and the influx of users, the workload of base station anomaly detection become more onerous.The variety type growth of the uplink interference of the base station are obvious, and there will be more than one type for one base station.It will be difficult to identify the uplink compound interference for the network operator.Meanwhile, there are not fixed frequency band positions and frequency bandwidths for the same interference type.For multiple interference sources case, the classification accuracy and generalization ability of traditional algorithms cannot meet the needs of the actual production environment.Therefore, this paper proposes the FETTrans network to transform the compound interference identification task into a multi-label classification in natural language processing.Inspired by the attention mechanism in Transformer and combined with the pattern recognition task, the multi-head attention calculation is performed on the constructed output vector and the input feature.The network transforms the unidirectional static projection method into a bidirectional dynamic adaptation method, which solves the problem of complex frequency band characteristics.After verification with different test sets collected in the actual production environment, the classification accuracy has been steadily increased more than 15%, and the mAP accuracy has reached 92%.The FETTrans network, as an end-to-end algorithm, can be adapted to other fault diagnosis in industry.
Improved VGG-16 Neural Network for Parameter Reduction

With the development of Deep Learning, image recognition technology has been applied in many aspects. And convolutional neural networks have played a key role in realizing image recognition under the increasing computing power and massive data. However, if developers want to implement the training of convolutional neural networks and achieve the subsequent applications in scenarios such as personal computers, IoT devices, and embedded platforms with low Graphics Processing Units(GPUs) memory, a large number of parameters during training of convolutional neural networks is a great challenge. Therefore, this paper uses depthwise separable convolution to optimize the classic convolutional neural network model VGG-16 to solve this problem. And the VGG-16-JS model is proposed using the Inception structure dimensionality reduction and depthwise separable convolution on the VGG-16 convolutional neural network model. Finally, this paper compares the classification success rates of VGG-16 and VGG-16-JS for the application scenario of the COVID-19 mask-wearing. A series of reliable experimental data show that the improved VGG-16-JS model significantly reduces the number of parameters required for model training without a significant drop in the success rate. It solves the GPU memory requirements for training neural networks to a certain extent.
A Patch-Based Composite Denoising Algorithm for Wireless Transmission

Many image denoising approaches in recent years are based on patch processing. Particularly, patch-based denoising algorithms under the low-rank (LR) model show outstanding performance in wireless transmission. However, constructing proper dictionaries is a key issue that affects the denoising results. On the basis of this idea, we propose a patch-based composite method for image denoising named similar patch LR (SPLR), which introduces a nonlocal model and structure similarity index measure (SSIM) to evaluate patches similarity. This evaluation method can explore the similarity of image patches to the full extent and construct a similar patches dictionary with an LR property. In addition, to improve the robustness of denoising, we divide the noise part into two parts, and use <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{0}$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2,0}$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-$</tex-math></inline-formula> norm, two different norm constraints to manage different types of noise. Extensive experiments show that the denoising performance and the robustness to noise of SPLR is better than that of the widely used and state-of-the-art, LR image denoising algorithms.
Skeleton-Based Human Action Recognition Using Motion and Orientation of Joints

Perceiving human actions accurately from a video is one of the most challenging tasks demanded by many real-time applications in smart environments. Recently, several approaches have been proposed for human action representation and further recognizing actions from the videos using different data modalities. Especially in the case of images, deep learning-based approaches have demonstrated their classification efficiency. Here, we propose an effective framework for representing actions based on features obtained from 3D skeleton data of humans performing actions. We utilized motion, pose orientation, and transition orientation of skeleton joints for action representation in the proposed work. In addition, we introduced a lightweight convolutional neural network model for learning features from action representations in order to recognize the different actions. We evaluated the proposed system on two publicly available datasets using a cross-subject evaluation protocol, and the results showed better performance compared to the existing methods.
Sound Design for Cinematic Virtual Reality: A State-of-the-Art Review

In the last decade, the rapid development of low-cost 360° cameras, Head Mounted Displays, smartphones, and open-source softwares have created new possibilities of immersive storytelling in virtual reality, popularly known as cinematic virtual reality or virtual reality films. Even in 360° videos, the visual experience remains limited to field of view of head mounted displays. Hence, the spatial audio is considered essential to create ‘Presence’ and ‘Immersion’. The viewer is part of the virtual environment with the freedom to interact, navigate, and choose the viewing direction. This presents challenge of sound recording, designing and playback. The recent development in spatial audio recording, sound synthesis and rendering has addressed some of these challenges. The current study presents the state-of-the-art of sound design in cinematic virtual reality through literature review and textual analysis of the relevant publication in the field since 2015. The objective is to identify further scope of studies. This review could be useful for filmmakers, sound designers, and scholars working in the field of cinematic virtual reality.
Increasing the Versatility of Leaky ReLU Using a Nonlinear Function

Activation functions are imperative for a neural network to learn and make sense of complicated inputs mapping to a decipherable wide-ranging output. For this purpose, there are voluminous activation functions available like rectified linear unit (ReLU), sigmoid activation function, exponential function, and many more. These functions have accomplished prodigious success in the past due to their applications. Each has diverse traits to cover to make the neural network less multifarious; however, they have faced criticism in recent days since their domain is limited to a scarce problem only. On the other hand, TensorFlow has developed as a new tool for making abstract implementation easy. It emboldens many implementations that can be done and work on complex problems like computer vision NLP, speech recognition, and much more. We have introduced an amalgamation of activation functions (ReLU, leaky ReLU, sigmoid function) not available in the pre-implemented function in the library. Our findings have contributed to increasing the efficiency of leaky ReLU activation function by a significant amount.
Effect of Different Encodings and Distance Functions on Quantum Instance-Based Classifiers

In the last years, we have witnessed the increasing usage of machine learning technologies. In parallel, we have observed the raise of quantum computing, a paradigm for computing making use of quantum theory. Quantum computing can empower machine learning with theoretical properties allowing to overcome the limitations of classical computing. The translation of classical algorithms into their quantum counter-part is not trivial and hides many difficulties. We illustrate and implement alternatives for the quantum nearest neighbor classifier focusing on the challenges related to data preparation and their effect on the performance. We show that, with certain data preparation strategies, quantum algorithms are comparable with the classic version, yet allowing for a theoretical reduction of the complexity for distances calculation.
Developing Arithmetic Optimization Algorithm for Travelling Salesman Problem

Nowadays, meta-heuristic algorithms based on biological species have grown increasingly familiar. In the general realm of swarm intelligence, the collective intelligence of diverse social insects like "ants, bees, wasps, termites, birds, and fish" has been researched to create a variety of meta-heuristic algorithms. The "traveling salesman problem (TSP)" represents a combinatorial optimization problem in which a salesman starts in one location and goes to all remaining cities in the lowest time feasible. TSP is a prominent problem because its instances may be used to address real-world issues, through the evaluation of the performance of novel algorithms. This paper develops the arithmetic optimization algorithm (AOA) for solving TSP. Here, the performance is evaluated by comparing with several heuristic-based algorithms in terms of convergence analysis. The efficacy of the suggested AOA for solving TSP is demonstrated by experimental findings.