Reviewerly/exHarmony huggingface dataset:
An Effective Algorithm for Specular Reflection Image Enhancement

Specular highlights often exist in real-world images due to the material property of objects and the capturing environments. High light in real-life scenes can reduce image quality and cause loss of image information, to overcome this, we propose an effective specular reflection image enhancement algorithm. First, the scene depth map of specular reflection image was obtained by the color attenuation prior method; then, two adaptive adjustment factors were developed to optimize the transmission map for specular reflection images and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> gradient minimization filter was used to eliminate halo artifacts. Finally, the nonuniform illumination compensation strategy based on the YUV color space was used to expand the dynamic range of the image and increase the saturation of the image. Experimental results via subjective and objective evaluations demonstrate that the proposed method can provide better visual quality than other methods and can effectively enhance the information in the specular reflection image.
Docker 101

Now that you understand a little bit better about how Docker works and why its popularity has exploded, in this chapter, you’ll learn some different terminology associated with Docker. You will also learn how to Install Docker and understand Docker terms such as images, containers, Dockerfiles, and Docker Compose. You also work with some simple Docker commands on creating, running, and stopping Docker containers.
Model-Driven Application Development for Evaluation and optimization of Automotive E/E-Architectures

Over the last decades, automobiles have developed from predominantly mechanical machines to driving computers, consisting of a large number of sensors, actuators, and electronic control units that use various types of communication busses to form large and complex cyber-physical systems which provide a variety of comfort and safety features for the driver. Such E/E-systems (electric/electronic systems) do not only require high availability and reliability, but also overall efficient architectures and topologies. Because of the already high (and constantly increasing) level of complexity of such systems, the evaluation and optimization of their corresponding architectures has become a big challenge. The need for evaluation and optimization methods that are capable of abstracting the systems’ complexity is evident for the automotive industry. In general, when it comes to designing and developing hard- and software systems, the paradigm of model-driven engineering emphasizes and supports the measures of abstraction, flexibility, and reusability to be able to grasp the complexity of modern systems. This paper presents a model-driven application for evaluating and optimizing automotive E/E-architectures as part of an integrated, model-based toolchain, developed with the Eclipse Modeling Framework.
Survey on Automatic Speech Recognition Systems for Indic Languages

For the past few decades, Automatic Speech Recognition (ASR) has gained a wide range of interest among researchers. From just identifying the digits for a single speaker to authenticating the speaker has a long history of improvisations and experiments. Human’s Speech Recognition has been fascinating problem amongst speech and natural language processing researchers. Speech is the utmost vital and indispensable way of transferring information amongst the human beings. Numerous research works have been equipped in the field of speech processing and recognition in the last few decades. Accordingly, a review of various speech recognition approaches and techniques suitable for text identification from speech is conversed in this survey. The chief inspiration of this review is to discover the prevailing speech recognition approaches and techniques in such a way that the researchers of this field can incorporate entirely the essential parameters in their speech recognition system which helps in overcoming the limitations of existing systems. In this review, various challenges involved in speech recognition process are discussed and what can be the future directives for the researchers of this field is also discussed. The typical speech recognition trials were considered to determine which metrics should be involved in the system and which can be disregarded.
Privacy-Preserving Swarm Learning Based on Homomorphic Encryption

Swarm learning is a decentralized machine learning method that combines edge computing, blockchain based point-to-point network and coordination while maintaining consistency without the need for a central coordinator for data integration between different medical institutions. Swarm learning solves problems of federated learning that the model parameters are still handled by the central custodian and star structure reduces fault tolerance etc. Although Swarm learning allows fair, transparent and highly regulated shared data analysis on the premise of meeting the privacy law, there will be inference attacks targeting shared model information between nodes, which will lead to privacy disclosure. Therefore, we propose the privacy-preserving Swarm learning scheme based on homomorphic encryption: we use the threshold Paillier cryptosystem to encrypt the shared local model information without affecting the accuracy of the model. In addition, we design a partial decryption algorithm to prevent privacy disclosure caused by aggregation model information. Experiments show that our scheme can guarantee strong privacy and has negligible influence on the accuracy of the final model.
Technology Behavior Model—Beyond Your Sight with Extended Reality in Surgery

Extended Reality Smart Glasses is a new pattern that uses extended reality technology to present a visual environment that combines the physical and virtual worlds. However, the surgical technique using Smart Glasses implementation is still unknown, to the infancy in clinical surgery, derived to the limits of existing technology. This study researched the acceptability and possibility of XRSG for medical experts. It combines human seen behavioral control with information technology research to construct a new “Extended Reality Technology Behavior Model” using method Technology Acceptance Model and Theory of Planned Behavior. To improve the accuracy of the study, statistical analysis, exploratory analysis, and cross-sectional research triangulation were used to collect data in five hospitals in Malaysia using a convenience sampling method and a questionnaire on behavioral influences. From the collected data, PLS-SEM analysis was used to reflect the relationship between variables. The strong positive results suggest that using XRSG by medical experts helps to improve the composition, interactivity, standardization, and clarity of medical images, resulting in increased efficiency and reduced procedure time and felt the usefulness and ease of use of XRSG through their behavior, providing a basis for technology acceptance in surgery.
The Influence of Avatar Personalization on Emotions in VR

In this paper, we investigate the impact of avatar personalization on perceived emotions. Avatar embodiment is a crucial aspect of collaborative and social virtual reality (VR) systems. Previous research found that avatar appearance impacts the acceptability of the virtual body and changes users’ behavior. While virtual embodiment has been extensively investigated, we know very little about how embodiment affects users’ experienced emotions. In a user study (N = 40), we applied an autobiographical recall method to evoke happiness and investigated the influence of different types of avatar embodiment (personalized same-gender, personalized opposite-gender, non-personalized same-gender, and non-personalized opposite-gender) on participants’ perceived emotion. We recorded both self-reported assessments and physiological data to observe participants’ emotional responses resulting from the emotions elicited by the use of different avatars. We found significant differences in happiness with the personalized same-gender avatar and the personalized opposite-gender avatar. We provide empirical evidence, demonstrating the influence of avatar personalization on emotions in VR. We conclude with recommendations for users and designers of virtual reality experiences.
Cryptography with Certified Deletion

We propose a unifying framework that yields an array of cryptographic primitives with certified deletion. These primitives enable a party in possession of a quantum ciphertext to generate a classical certificate that the encrypted plaintext has been information-theoretically deleted, and cannot be recovered even given unbounded computational resources. We obtain our results by developing a novel proof technique to argue that a bit b has been information-theoretically deleted from an adversary’s view once they output a valid deletion certificate, despite having been previously information-theoretically determined by the ciphertext they held in their view. This technique may be of independent interest.
Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma

Most recently, the pathology diagnosis of cancer is shifting to integrating molecular makers with histology features. It is a urgent need for digital pathology methods to effectively integrate molecular markers with histology, which could lead to more accurate diagnosis in the real world scenarios. This paper presents a first attempt to jointly predict molecular markers and histology features and model their interactions for classifying diffuse glioma bases on whole slide images. Specifically, we propose a hierarchical multi-task multi-instance learning framework to jointly predict histology and molecular markers. Moreover, we propose a co-occurrence probability-based label correction graph network to model the co-occurrence of molecular markers. Lastly, we design an inter-omic interaction strategy with the dynamical confidence constraint loss to model the interactions of histology and molecular markers. Our experiments show that our method outperforms other state-of-the-art methods in classifying diffuse glioma, as well as related histology and molecular markers on a multi-institutional dataset.
MAST: Mask-Accelerated Shearlet Transform for Densely-Sampled Light Field Reconstruction

Shearlet Transform (ST) is one of the most effective algorithms for the Densely-Sampled Light Field (DSLF) reconstruction from a Sparsely-Sampled Light Field (SSLF) with a large disparity range. However, ST requires a precise estimation of the disparity range of the SSLF in order to design a shearlet system with decent scales and to pre-shear the sparsely-sampled Epipolar-Plane Images (EPIs) of the SSLF. To overcome this limitation, a novel coarse-to-fine DSLF reconstruction method, referred to as Mask-Accelerated Shearlet Transform (MAST), is proposed in this paper. Specifically, a state-of-the-art learning-based optical flow method, FlowNet2, is employed to estimate the disparities of a SSLF. The estimated disparities are then utilized to roughly estimate the densely-sampled EPIs for the sparsely-sampled EPIs of the SSLF. Finally, an elaborately-designed soft mask for a coarsely-inpainted EPI is exploited to perform an iterative refinement on this EPI. Experimental results on nine challenging horizontal-parallax real-world SSLF datasets with large disparity ranges (up to 35 pixels) demonstrate the effectiveness and efficiency of the proposed method over the other state-of-the-art approaches.
Transfer Learning for Predicting Software Faults

This paper investigates a transfer learning application for predicting software faults. Detecting faulty modules in software projects is challenging due to two main issues 1) the low quality of existing handcrafted features leads to the bad performance of traditional learning models and 2) the shortage of annotated data hinders applying deep neural networks. Recently, transfer learning is a good solution to train deep neural networks with insufficient data. Our experiments for tasks of within-project and cross-project software fault prediction have shown the transferable possibility among project data. As a result, the performance of the base model is significantly improved and achieves competitive results with the state of the art method.
Modified JIT-Based Solar Irradiance Prediction Methods

One of the effective methods for solar irradiance prediction is based on the Just-In-Time (JIT) modeling technique. To use in micro grid management, this paper presents two techniques to improve the prediction accuracy of the one-day ahead JIT-based prediction method due to Suzuki et al. (2011). The first technique is to update the solar irradiance prediction more frequently in accordance with the updating period of weather forecast data. The second technique is to make a correction to the JIT-based prediction online based on the most recent solar irradiance measurements.
A Retrospective on Agile Transformations: Survey Results on Agility of German Organisations

Until recently, Agile was considered as a set of principles and practices relevant only to software development projects. However, Agile is now spreading to other parts and types of organisations, turning into a booming field of various frameworks and methods. While everyone refers to the Agile Manifesto [1], [2], "Agile" is perceived and, thus, implemented differently. Alignment on the question "What is "Agile" to you?" contributes to the common ground and "common language" for adoption. Understanding how exactly Agile an organisational unit is, in turn, provides transparency and is the means for consistent adoption. This paper represents a retrospective of the survey on agile transformation devoted to these questions.
Peramalan Penyebaran Jumlah Kasus Covid19 Provinsi Jawa Tengah dengan Metode ARIMA

Covid 19 virus disease (corona virus disease), a name given by WHO for patients with coronavirus infection, was first reported from the city of Wuhan, China at the end of 2018. The spread occurred rapidly and created a new pandemic threat. The purpose of this study was to analyze the development of a pandemic in Indonesia, especially in Central Java province. The method used is ARIMA modeling, to determine the percentage of covid19 developments in Central Java. By using the ARIMA method, the ARIMA model (1,1,1) is obtained as a suitable model for the spread of the number of positive cases of Covid-19. Based on this model, with the results that the number of spread of the number of positive cases can be predicted over the next 10 periods, with the result that the number of spread of the number of cases of the Covid-19 virus has decreased from the ece period.
Classification Models of Text: A Comparative Study

Classification, as a problem, has far-reaching applications in data mining. Most notably, text classification has skyrocketed in usage and importance, having implications in education, politics, and even finance. As a process, classification generally occurs as follows: preprocessing, feature engineering, dimension decomposition, model selection, and model evaluation. This paper will provide a brief overview of each stage, as well as survey and compare popular classification algorithms.
An Improved Whale Optimization Algorithm and Its Application to Power Generation in Cascade Reservoir

Nowadays, there is a very popular artificial intelligence algorithm called whale optimization algorithm (WOA). WOA is obtained through the special bubble net foraging process of humpback whales. It has a special search mechanism, and is very helpful to solve some complex optimization problems and large scale. An improved WOA is implemented to optimize the cascade reservoir power generation. Firstly, by introducing nonlinear time-varying adaptive weights, the WOA performance in the local optimization and global exploration stages is improved; secondly, the differential mutation perturbation factor is introduced in the shrinking and surrounding stage of the whale algorithm to prevent premature convergence. In addition, the logarithmic spiral search method of whale individuals has been improved so that the ability to solve the algorithm traversal can be found. Experimental results show that it has a great improvement in accuracy and convergence compared with the original WOA in the optimal dispatch model of cascade reservoir power generation.
Multi-Stage CNN-Based Monocular 3D Vehicle Localization and Orientation Estimation

This paper aims to design a 3D object detection model from 2D images taken by monocular cameras by combining the estimated bird’s-eye view elevation map and the deep representation of object features. The proposed model has a pre-trained ResNet-50 network as its backend network and three more branches. The model first builds a bird’s-eye view elevation map to estimate the depth of the object in the scene and by using that estimates the object’s 3D bounding boxes. We have trained and evaluate it on two major datasets: a syntactic dataset and the KIITI dataset.
Genre and Stylistic Features of the Modern Audiobook

Modern technological conditions make it possible to create, quickly replicate and use audio books conveniently. Audio books are one of the fastest growing segments of the global publishing market. Informative issues of creating audio books, not technological ones, are in the research focus of the article. The content of an audiobook is a voiced text that refers to the “auditory literature”. Assessments of the quality of the auditory literature are polar. On the one hand, it is considered secondary to the original literary text; on the other hand, it is a self-contained artistic phenomenon with its own aesthetic nature. In this article, an audiobook is considered precisely in the aspect of its artistic value, which is highlighted when speaking about the genre nature of the voiced text. The genre features of the voiced text in this study are identified taking into account the communicative features of its formal-stylistic features. The communicative nature of the audiobook genre is associated with two types of reading, which reflect the opposite positions of the two participants in communication. On the one hand, this is an expres-sive reading aloud, which can also be defined as staged reading. Genetically, this type of reading is associated with public performances of artists and initially assumed live reading. Further, this type of reading is transformed into the genre of radio plays, called “theater at the microphone”. In modern communicative practices of creating and repli-cating audio content, including one related to the actor’s readings of works of art, there is no binding to time and place. On the other hand, this is auditory reading, a modifica-tion of which is audio reading in modern technological conditions. If auditory reading is the first reading practice of a child mastering books from the voice of a parent, then audio reading is the choice of an adult who can read. The acoustic representation of a literary work is associated not only with the performance of elementary technical characteristics of sound, but also with the introduction of a certain aesthetic value into it. The creative translation of a literary text from verbal to acoustic should preserve its value in the aesthetic plane, without reducing it to a purely pragmatic one. Actualization of the aesthetic value of an audiobook outside of its paper format is associated with the principles of its directing and editorial preparation – the principles associated with the implementation of the stylistic characteristics of the genre form of an audiobook. Translation of a verbal literary text into an audio one is carried out as a result of comparing reading a book to dramatic action. In this case, the forming element of the genre becomes the sounding text itself. In the case of audio books, the reader’s voice as a performer’s instru-ment and the musical noise accompaniment of the text read is a style-forming genre element. The article traces the publishing strategies for the embodiment of the formal-stylistic features of the audiobook genre in the context of modern audio cultural practices.
FLEX: Unifying Evaluation for Few-Shot NLP

Few-shot NLP research is highly active, yet conducted in disjoint research threads with evaluation suites that lack challenging-yet-realistic testing setups and fail to employ careful experimental design. Consequently, the community does not know which techniques perform best or even if they outperform simple baselines. We formulate desiderata for an ideal few-shot NLP benchmark and present FLEX, the first benchmark, public leaderboard, and framework that provides unified, comprehensive measurement for few-shot NLP techniques. FLEX incorporates and introduces new best practices for few-shot evaluation, including measurement of four transfer settings, textual labels for zero-shot evaluation, and a principled approach to benchmark design that optimizes statistical accuracy while keeping evaluation costs accessible to researchers without large compute resources. In addition, we present UniFew, a simple yet strong prompt-based model for few-shot learning which unifies the pretraining and finetuning prompt formats, eschewing complex machinery of recent prompt-based approaches in adapting downstream task formats to language model pretraining objectives. We demonstrate that despite simplicity UniFew achieves results competitive with both popular meta-learning and prompt-based approaches.
Logical Form – Not logical enough for logic, not linguistic enough for linguistics

In mainstream generative grammar (MGG), Logical Form (LF) is at the same time a syntactic representation delivered by the grammar and a representation that can be truth-conditionally evaluated. Iacona (2018) argues however that formal logic raises a problem for MGG's intrinsicalist notion of LF: logic requires that the logical form assigned to a sentence should not depend solely on the sentence itself, but also on the logical relations the sentence bears to other propositions in the language, making logicians' logical form dependent on something outside the sentence. This paper argues that LF is also not linguistic enough for linguistics because it is modelled on formal logic rather than on careful analysis of natural language and the natural articulations of linguistic utterances. This leads to a misidentification of the basic linguistic unit, which is not the sentence, and a misanalysis of the sentence's basic structure. The conclusion argues that the only methodologically sound basis for linguistic analysis is not truth, nor the sentence that expresses it, but rather the means that allow the speaker to produce the utterance in the first place, the stable pairings of meaning and sound corresponding to the units that the speaker has stored in memory.