Reviewerly/exHarmony huggingface dataset:
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020.

This paper presents the results and insights from the black-box optimization (BBO) challenge at NeurIPS 2020 which ran from July-October, 2020. The challenge emphasized the importance of evaluating derivative-free optimizers for tuning the hyperparameters of machine learning models. This was the first black-box optimization challenge with a machine learning emphasis. It was based on tuning (validation set) performance of standard machine learning models on real datasets. This competition has widespread impact as black-box optimization (e.g., Bayesian optimization) is relevant for hyperparameter tuning in almost every machine learning project as well as many applications outside of machine learning. The final leaderboard was determined using the optimization performance on held-out (hidden) objective functions, where the optimizers ran without human intervention. Baselines were set using the default settings of several open-source black-box optimization packages as well as random search.
Facial Mole Detection Approach for Suspect Face Identification using ResNeXt-50

The most useful applications of face recognition are criminal face identification. There is numerous facial feature help in recognizing the criminal face like nose shape, eyes color, skin tone, etc. Face moles also play a vital role in recognizing the criminal face. Previous methods were able to detect facial spots which include moles, scars, pimples, etc but fail to discriminate the face mole from other facial spots. This paper presents a mole detection classifier that isolates the mole from other facial spots. In the proposed approach, first, a person's face is identified, following which the spot on the face is determined using Normalized Cross-Correlation, and the kind of spot is determined using the Modified ResNeXt-50 CNN model. The effectiveness of the proposed model is demonstrated on the YALE image dataset. Experimental evaluation and comparison with the state-of-the-art method shows that the proposed model outperforms it.
Probing Pre-trained Language Models for Semantic Attributes and their Values

Pretrained Language Models (PTLMs) yield state-of-the-art performance on many Natural Language Processing tasks, including syntax, semantics and commonsense reasoning.In this paper, we focus on identifying to what extent do PTLMs capture semantic attributes and their values, e.g. the relation between rich and high net worth.We use PTLMs to predict masked tokens using patterns and lists of items from Wikidata in order to verify how likely PTLMs encode semantic attributes along with their values.Such inferences based on semantics are intuitive for us humans as part of our language understanding.Since PTLMs are trained on large amount of Wikipedia data, we would assume that they can generate similar predictions.However, our findings reveal that PTLMs perform still much worse than humans on this task.We show an analysis which explains how to exploit our methodology to integrate better context and semantics into PTLMs using knowledge bases.
Demo: The First Open AR Cloud Testbed

We present the first deployment of an open, scalable and distributed spatial computing platform. We show in action its extensible protocols for discovering spatial services and spatial content in a geographic area, representing poses of real and virtual cameras and objects, exchanging content records, and interfacing with spatial computing services like visual localization. We also demonstrate the usage of the platform through a WebXR reference client in the first city-wide prototype testbed in Bari, Italy.
Distributed Service Composition in Internet of Services

To meet coarse-grained user requirements, many service composition approaches have been put forward in the past 20 years. The service composition constructs a service workflow by aggregating services to meet users’ functional and non-functional requirements. In the era of the Internet of Services (IoS), service solutions are constituted by services deployed on distributed platforms and owned by different providers. For reasons such as commercial confidentiality, different providers or platforms (called "IoS nodes") generally do not share complete information of services to other nodes. Thus, how to efficiently construct optimal service composite solutions for complex user requirements under this distributed service environment is a great challenge. In this paper, we propose a three-stage distributed service composition method with coordination protocols and local algorithms. In the first stage, when an IoS node receives a requirement (the initial node), it uses a centralized service composition algorithm and broadcasts sub-requirements it can’t meet to all other nodes through a requirement broadcasting protocol. In the second stage, the initial node uses a node selection algorithm to assign a node to provide the service for each sub-requirement. In the third stage, the nodes coordinate their services with other nodes through a constraint coordination protocol based on the upper confidence bound algorithm and the initial node obtains a composite service solution. The experimental results demonstrate that our method outperforms other distributed service composition methods in terms of efficiency and solution quality.
Convolutional Neural Network Denoising of Focused Ion Beam Micrographs

Most research on deep learning algorithms for image denoising has focused on signal-independent additive noise. Focused ion beam (FIB) microscopy with direct secondary electron detection has an unusual Neyman Type A (compound Poisson) measurement model, and sample damage poses fundamental challenges in obtaining training data. Model-based estimation is difficult and ineffective because of the nonconvexity of the negative log likelihood. In this paper, we develop deep learning-based denoising methods for FIB micrographs using synthetic training data generated from natural images. To the best of our knowledge, this is the first attempt in the literature to solve this problem with deep learning. Our results show that the proposed methods slightly outperform a total variation-regularized model-based method that requires time-resolved measurements that are not conventionally available. Improvements over methods using conventional measurements and less accurate noise modeling are dramatic - around 10 dB in peak signal-to-noise ratio.
Designing a System for Data-driven Risk Assessment of Solar Projects

Solar energy is the fastest growing source of renewable energy worldwide, and is set to grow at an unprecedented pace for the coming years. Large scale solar energy projects now compete with conventional energy production and offer attractive returns to investors. Solar energy projects have a projected lifetime of over 25 years and while the returns are attractive, investors rarely oversee the risks that impact their Return on Investment (ROI) over the long term. In the wake of increasingly fiercer competition among PV module manufacturers, quality often takes a backseat. In this project, we propose a prognostic solution contrary to existing reactive approaches. We develop a data-driven decision support system (DSS) for technical derisking of utility scale solar energy projects. This system can provide project stakeholders insight into risks associated to different manufacturers. The system is based on data gathered by Sinovoltaics Group, a leading solar quality assurance company with 10+ years of experience. Using information extraction algorithms, useful data is extracted from a large number of quality assurance reports from Sinovoltaics Group, compiled in a database and analyzed for risk assessment leading to a DSS.
Controlling Prosody in End-to-End TTS: A Case Study on Contrastive Focus Generation

While End-2-End Text-to-Speech (TTS) has made significant progresses over the past few years, these systems still lack intuitive user controls over prosody.For instance, generating speech with fine-grained prosody control (prosodic prominence, contextually appropriate emotions) is still an open challenge.In this paper, we investigate whether we can control prosody directly from the input text, in order to code information related to contrastive focus which emphasizes a specific word that is contrary to the presuppositions of the interlocutor.We build and share a specific dataset for this purpose and show that it allows to train a TTS system were this fine-grained prosodic feature can be correctly conveyed using control tokens.Our evaluation compares synthetic and natural utterances and shows that prosodic patterns of contrastive focus (variations of Fo, Intensity and Duration) can be learnt accurately.Such a milestone is important to allow, for example, smart speakers to be programmatically controlled in terms of output prosody.
Exploring the Relationships between Distractibility and Website Layout of Virtual Classroom Design with Visual Saliency

Previous studies reveal two different opinions about the relationship between distractibility and media multitasking, which is the simultaneous use of different media at the same time. Some studies suggest that media multitasking is associated with increased distraction, while in contrast, other studies found there is no significant correlation. The aim of this paper is to examine whether there is correlation between distractibility and different website layouts of a virtual classroom for online education, a form of media multitasking. This is achieved through the use of eye tracking technology to record scanpaths and analyze eye tracking metrics through a spatial scale with the principle of visual saliency, Graph-Based Visual Salience (GBVS), between different areas of interest (AOI). This paper found that visual distraction is significantly correlated between online learning performance and six typical website layouts of virtual classrooms. These layouts were classified through 30 existing online learning platforms, according to the different locations of the chat room, instructor, main display area, navigation tabs, as well as participant list in the interface. The results show that the value of visual distraction is negatively correlated with academic performance (r = −0.666, P < .001). The research from the perspective of visual saliency can provide valuable support for physiological indicator testing tools of visual distraction, and highlights the application of eye tracking metrics in behavior.
Application of journey maps to the development of emergent sustainability‐oriented technologies: Lessons for user involvement in agriculture

Abstract This article foregrounds the challenges and necessity of promoting user involvement in traditional sectors, especially those that constitute strong pillars for sustainable development. As a case in point, the emergence of precision agriculture technologies requires novel methods to integrate users in innovation processes. Accordingly, this article discusses three applications of journey maps developed in collaboration with users and other stakeholders in the context of agriculture. We illustrate how design‐thinking tools such as journey maps offer the means to explore user experiences, spotting previously unknown needs or problems, generate value propositions with meaning and relevance, and foresee implementation issues not directly related to the technology in focus. This research, thus, contributes by first describing applications of journey maps which are relevant for traditional sectors that are undergoing disruption, and second, by highlighting the broader usefulness of such tools for facilitating coordination among the diverse actors who are ultimately central to innovation adoption and success.
Blockchain based Lebesgue interpolated Gaussian secured information sharing for pharma supply chain

Supervising the product's integrity and procedures in a multi-participant supply chain environment is a noteworthy issue. In recent years blockchain technology (BT) has made an appearance as a paramount model, as it bestows secure tracking, benchmark, and trust formation between stakeholders in a cost-efficient solution. To control the foregoing concerns, data analytics is indispensable on blockchain-based secure data and hence elevates the significance of surfacing technology Machine Learning (ML). The reliability of data and its distribution are very pivotal in ML to enhance the accuracy of results. In this paper, a blockchain-based secured information sharing method called Lebesgue IntegrableConsensus and Interpolated Gaussian Learning-based (LIC-IGL) authentication to provide pharmaceutical data integrity and security is proposed. The LIC-IGL method is split into two sections. First, validation of block is performed by applying the Lebesgue Integrable Consensus model. The second step involves the authentication process carried out by employing the Adaptive Support Vector Machine Authentication model via smart contracts. Finally, upon successful authentication, distinct products are provided between and within the blocks, therefore, ensuring secured pharmaceutical product sharing. The effectiveness of the proposed and existing methods is compared with certain parameters such as latency, authentication accuracy, and false positive rate with respect to distinct numbers of products. The results acquired from the experiments exhibit a superior execution of the proposed method upon comparison with the state-of-the-art blockchain-based authentication methods and it shows enhanced simulated results with good authentication accuracy, minimizing the latency and false positive rate.
Macro-semantic rhythmic structures in popular author songs

Certain author song of the 1950s &amp;ndash; 1980s are characterized with such structural peculiarity as semantic rhythms. This pattern manifests itself in two forms: dialectical juxtaposition of the particular and the general (PG-rhythm); as well as the conflict of non-entropic and entropic processes, order and chaos (OC-rhythm). For broader understanding of this phenomenon, it is necessary to determine its place within the framework of the author song as a large-scale cultural phenomenon. The author mass processing of popular compositions using the method of almost full enumeration. The research material includes all 5 thematic national collections that feature 487 songs. The analysis of the lyrics reveals the total number of works with the initial semantic rhythmics: 43% with the PG-structure and 23% with the OC-structure. The application of various strategies of analysis indicates that the sampling of no less than 70-80 compositions should be used for achieving accurate results in partial studies (for example, by authors) . In the course of research, the author determines new versions of manifestation of the semantic structures: PG-structure of fable-type, as well as variability of Brodsky's saw in songs with entropic rhythmics. There are also songs with intersection of both types of semantic rhythms. Correlation is established between the social processes of cultural environment associated with the author song and the dynamics of semantic rhythms in popular compositions. In the early 1970s, a bifurcation point is noticed in the moods of the corresponding social community.
Application and Realistic Dilemma of VR Technology in Film and Television Production

Abstract VR was formally put into research in 1989. Through continuous development, it has gradually attracted attention from various fields. By now, a new viewing mode has emerged out of it. To follow up the new viewing trend, city promos also begin to adopt this technology. Under this background, this article attempts to explain the current combination of VR and film and television production, analyzes its advantages and disadvantages, and explores the practical difficulties and development prospects of this mode.
Tracking of Dance Limb Movement Trajectory Based on Kinect Bone Data

In dance training, it is necessary to carry out automatic trajectory tracking and feature analysis of movements, to carry out dance training guidance, and to propose a dance limb movement trajectory tracking method based on Kinect bone data. The multi-resolution frame scanning technology is adopted to collect machine images of dance limb movements, Kinect bone data feature detection is carried out on the collected high-resolution dance limb movement images, a three-dimensional reconstruction method of Kinect bone data is adopted to carry out region segmentation of dance limb movements, and a Kinect bone data tracking model is combined to realize machine vision block template matching processing of dance limb movements. Dancing limb motion trajectory tracking and feature extraction are carried out in Kinect bone data distribution space. Wavelet detection technology is used for fuzzy identification of dancing limb motion. Kinect bone data fusion is used to improve the resolution and adaptability of dancing limb motion trajectory tracking. Fuzzy image enhancement and feature point detection methods are used to realize optimization of dancing limb motion trajectory tracking. The simulation results show that this method has good adaptability and high degree of feature identification for dance limb movement trajectory tracking, which improves the ability of feature extraction and adaptive trajectory tracking of dance limb movement.
Assessing the performance of XDP and AF_XDP based NFs in edge data center scenarios

While servers in traditional data centers can be specialized to run either CPU-intensive or network-intensive workloads, edge data centers need to consolidate both on the same machine(s) due to the reduced number of servers. This paper presents some preliminary experiments to determine how to improve the overall throughput of the above servers, being XDP and AF_XDP the two main technologies into play.
Neural Policy Style Transfer

Style Transfer has been proposed in a number of fields: fine arts, natural language processing, and fixed trajectories. We scale this concept up to control policies within a Deep Reinforcement Learning infrastructure. Each network is trained to maximize the expected reward, which typically encodes the goal of an action, and can be described as the content. The expressive power of deep neural networks enables encoding a secondary task, which can be described as the style. The Neural Policy Style Transfer (NPST)1 algorithm is proposed to transfer the style of one policy to another, while maintaining the content of the latter. Different policies are defined via Deep Q-Network architectures. These models are trained using demonstrations through Inverse Reinforcement Learning. Two different sets of user demonstrations are performed, one for content and other for style. Different styles are encoded as defined by user demonstrations. The generated policy is the result of feeding a content policy and a style policy to the NPST algorithm. Experiments are performed in a catch-ball game inspired by the Deep Reinforcement Learning classical Atari games; and a real-world painting scenario with a full-sized humanoid robot, based on previous works of the authors. The implementation of three different Q-Network architectures (Shallow, Deep and Deep Recurrent Q-Network) to encode the policies within the NPST framework is proposed and the results obtained in the experiments with each of these architectures compared.
Data augmentation and shadow image classification for shadow detection

Shadow detection is an important branch of computer vision. Recently, convolutional neural network (CNN)-based methods for shadow detection have achieved better performance than methods based on manually designed features. However, CNNs are extremely hungry for data and the training of CNN-based shadow detector requires time-consuming and expensive pixel-level annotations. To alleviate this problem in shadow detection, a method of data augmentation based on generative adversarial network (GAN), named ShadowGAN, has been proposed. Given a shadow mask and a shadow-free image, our ShadowGAN can generate shadow images with labels. To guide the training of ShadowGAN and get more realistic shadow images, L 1 ${{\cal L}_1}$ loss is further implemented to impose a restriction between real shadow images and generated shadow images. The effectiveness of ShadowGAN is demonstrated by training existing shadow detectors on enlarged dataset. In addition, to better make use of shadow-free images in shadow detection, shadow image classification task is added for the shadow detectors. Experiments show that this task can guide the feature extraction network to learn more robust shadow features. At last, these two methods are combined and a better performance of shadow detection is achieved.
Using Hand Tracking and Voice Commands to Physically Align Virtual Surfaces in AR for Handwriting and Sketching with HoloLens 2

In this paper, we adapt an existing VR framework for handwriting and sketching on physically aligned virtual surfaces to AR environments using the Microsoft HoloLens 2. We demonstrate a multimodal input metaphor to control the framework’s calibration features using hand tracking and voice commands. Our technical evaluation of fingertip/surface accuracy and precision on physical tables and walls is in line with existing measurements on comparable hardware, albeit considerably lower compared to previous work using controller-based VR devices. We discuss design considerations and the benefits of our unified input metaphor suitable for controller tracking and hand tracking systems. We encourage extensions and replication by providing a publicly available reference implementation (https://go.uniwue.de/hci-otss-hololens).
Design and Simulation of Adaptive Neuro-Fuzzy Inference System Inverse Controller for a Coupled Tank System

Level control of coupled tank systems is an important issue in the industry's process in the presence of nonlinearity. This paper presented an inverse control method based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The ANFIS is utilized to design and implement the nonlinear controller, which has the inverse transfer function of the system. There is no conventional direct feedback in the control loop just that in the enhancement period. The system output is used by an adaptive algorithm to adjust the parameters of the controller. The ANFIS inverse controller has been tested on a simulated coupled tank system and for the sake of results comparison, the output response of the simulated system has been compared using the ANFIS inverse controller and the classical PID controller under the same conditions. The simulation of the coupled tank system and ANFIS controller software has been designed and implemented using the LabVIEW software and MathScript facility.
Blockchain Technology Applications for Next Generation

Blockchain technology is a technological breakthrough that can lead to the next generation of information and communication technology (ICT), enabling low-cost, open-ended, and tamper-resistant data access, high availability, and transparency. Originally intended to fund Bitcoin’s digital money, it is expected to be expanded to a broad spectrum of financial applications as well as to other industries, such as delivery and shared economies. This larger deployment calls for a range of technical challenges to be tackled through the clouding of data privacy protections and improved processing speed, and the Fujitsu Laboratory is working on several related R&D projects. This research outlines blockchain technologies and sample applications, describes encryption technology in the business sense, and examines Fujitsu’s communication activities.