Reviewerly/exHarmony huggingface dataset:
not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget.

BigGAN is the state-of-the-art in high-resolution image generation, successfully leveraging advancements in scalable computing and theoretical understanding of generative adversarial methods to set new records in conditional image generation. A major part of BigGAN's success is due to its use of large mini-batch sizes during training in high dimensions. While effective, this technique requires an incredible amount of compute resources and/or time (256 TPU-v3 Cores), putting the model out of reach for the larger research community. In this paper, we present not-so-BigGAN, a simple and scalable framework for training deep generative models on high-dimensional natural images. Instead of modelling the image in pixel space like in BigGAN, not-so-BigGAN uses wavelet transformations to bypass the curse of dimensionality, reducing the overall compute requirement significantly. Through extensive empirical evaluation, we demonstrate that for a fixed compute budget, not-so-BigGAN converges several times faster than BigGAN, reaching competitive image quality with an order of magnitude lower compute budget (4 Telsa-V100 GPUs).
Scalable and robust latent trajectory class analysis using artificial likelihood

Abstract Latent trajectory class analysis is a powerful technique to elucidate the structure underlying population heterogeneity. The standard approach relies on fully parametric modeling and is computationally impractical when the data include a large collection of non‐Gaussian longitudinal features. We introduce a new approach, the first based on artificial likelihood concepts, that avoids undue modeling assumptions and is computationally tractable. We show that this new method provides reliable estimates of the underlying population structure and is from 20 to 200 times faster than conventional methods when the longitudinal features are non‐Gaussian. We apply the approach to explore subgroups among research participants in the early stages of neurodegeneration.
Quantum entanglement purification assisted by classical phase noise

Entanglement purification is a vital protocol to produce a high-quality entangled state from an ensemble of identical states. Based on the particular scheme of entanglement purification [Phys. Rev. A 87, 052316 (2013)], the effect of phase fluctuation is investigated. The convergence pattern of the initial states can be divided into two kinds of regions, corresponding to the purified outcomes being a maximally entangled state (MES) or a separable state. And there is the fractal-like structure near the boundary between these two regions. It is found that the phase noise plays a positive role in generating an MES for an initial states near some fractal boundary, which can only become the separable one if the noise is absent. It is also found that the minimal iteration steps to achieve the maximally entangled state with the phase noise can be decreased, which can save the resource in the protocol.
Computing WHERE-WHAT Classification Through FLIKM and Deep Learning Algorithms

Object recognition has gained substantial importance in the artificial intelligence technology to visualize, understand and to take a decision. Many complex algorithms achieve AI utilities. The application of these object recognition methods extends from the medical field to wide variety of other sectors, especially industries. As per the literature survey, it defines that many systems or algorithms are available to process the recognition, but accuracy toward invariant 2D data with scaling, translation, rotation and partial properties is not up to the mark. In this paper, an efficient, systematic approach is developed to understand the place or a situation of a particular scene. Optimized deep learning in association with a clustering algorithm (FLIKM) is used in this approach to build an intelligent self-surveillance system. The experimental results of the state-of-the-art system evaluated with various benchmark datasets show an acceptable accuracy rate of 94 approximately.
Broadband Diffractive Neural Networks

Broadband diffractive neural networks that process a continuum of wavelengths for task- specific component design and all-optical inference are presented. This framework is experimentally demonstrated by designing spectral filters and a spatially-controlled demultiplexer at THz wavelengths.
Cochrane corner: digital contact tracing technologies in epidemics.

Contact tracing is a public health measure implemented to control the spread and break the chains of transmission of an infectious disease. It is done by identifying, assessing, and managing people who have been exposed to an infectious disease to prevent onward transmission. We summarize findings from a rapid Cochrane review that included cohort and modelling studies to assess the benefits and harms of digital solutions for identifying contacts of confirmed positive cases of an infectious disease. The review included 12 studies, which assessed digital contact tracing for the following infectious diseases: Ebola, tuberculosis, pertussis and coronavirus disease 2019 (COVID-19). This review revealed low-certainty evidence of a decrease in secondary cases of the targeted infectious disease, if digital contact tracing was used. However, it is uncertain from the currently available evidence whether digital contact tracing would produce more reliable counts of contacts and reduce the time taken to complete contact tracing. Therefore, implementation of digital contact tracing in the context of the ongoing coronavirus pandemic in African countries should be accompanied by a robust monitoring and evaluation framework. There should be an evaluation and documentation of the benefits, cost-effectiveness, acceptability, feasibility, equity impacts, and unintended consequences of the intervention.
LTAI BERBASIS TEKNOLOGI BLOCKCHAIN UNTUK MENINGKATKAN ALEXA RANK

The development of technology and information that is taking place today has an impacton the creation of many online community websites that provide, provide various kinds ofinformation that are served and packaged in cement form, perhaps, such as the AlphabetIncubator Site. The Alphabet Incubator has a lot of information contained in the use of theblockchain according to the allocated LTAI schedule. The Alphabet Incubator website is a startupcommunity site based on the creativepreneur company. Alphabet Incubator has a goal with a veryhigh potential in increasing the traffic rank of the Alphabet Incubator website as seen from thesupported LTAI schedules. Blockchain research in recent years has produced a need to conductresearch studies. Through this research, there are 3 (three) issues that are being discussedregarding improving the ranking of the Alphabet Incubator website by using the AlphabetIncubator website to create an LTAI schedule. To improve quality, use blockchain on the LTAIschedule on the Alphabet Incubator website. To increase the traffic rank of the Alphabet Incubatorwebsite and to support the development of the Alphabet Incubator, the implementation is carriedout by using the blockchain on the LTAI schedule that has been made by students. Resulting inblockchain utilization activities on the LTAI schedule and in order to increase the rank of theAlphabet Incubator website using Alexa Rank.Keywords—Alphabet Incubator,blockchain, Rank, Schedule.
&lt;p&gt;&lt;strong&gt;Patronymic particle and bibliographic citation&lt;/strong&gt;&lt;/p&gt;

We offer a review of the correct way of writing the names of authors with a patronymic particle in scientific papers, bibliographic lists and indexes.
Hybrid Space Learning for Language-based Video Retrieval

This paper attacks the challenging problem of video retrieval by text. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described exclusively in the form of a natural-language sentence, with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is crucial. To that end, the two modalities need to be first encoded into real-valued vectors and then projected into a common space. In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Our novelty is two-fold. First, different from prior art that resorts to a specific single-level encoder, the proposed network performs multi-level encoding that represents the rich content of both modalities in a coarse-to-fine fashion. Second, different from a conventional common space learning algorithm which is either concept based or latent space based, we introduce hybrid space learning which combines the high performance of the latent space and the good interpretability of the concept space. Dual encoding is conceptually simple, practically effective and end-to-end trained with hybrid space learning. Extensive experiments on four challenging video datasets show the viability of the new method.
Information Laundering for Model Privacy

In this work, we propose information laundering, a novel framework for enhancing model privacy. Unlike data privacy that concerns the protection of raw data information, model privacy aims to protect an already-learned model that is to be deployed for public use. The private model can be obtained from general learning methods, and its deployment means that it will return a deterministic or random response for a given input query. An information-laundered model consists of probabilistic components that deliberately maneuver the intended input and output for queries to the model, so the model's adversarial acquisition is less likely. Under the proposed framework, we develop an information-theoretic principle to quantify the fundamental tradeoffs between model utility and privacy leakage and derive the optimal design.
GazeBase: A Large-Scale, Multi-Stimulus, Longitudinal Eye Movement Dataset

This manuscript presents GazeBase, a large-scale longitudinal dataset containing 12,334 monocular eye-movement recordings captured from 322 college-aged subjects. Subjects completed a battery of seven tasks in two contiguous sessions during each round of recording, including a - 1) fixation task, 2) horizontal saccade task, 3) random oblique saccade task, 4) reading task, 5/6) free viewing of cinematic video task, and 7) gaze-driven gaming task. A total of nine rounds of recording were conducted over a 37 month period, with subjects in each subsequent round recruited exclusively from the prior round. All data was collected using an EyeLink 1000 eye tracker at a 1,000 Hz sampling rate, with a calibration and validation protocol performed before each task to ensure data quality. Due to its large number of subjects and longitudinal nature, GazeBase is well suited for exploring research hypotheses in eye movement biometrics, along with other emerging applications applying machine learning techniques to eye movement signal analysis.
Runtime Verification of Contracts with Themulus

Contracts regulating the behaviour of multiple interacting parties go beyond the notion of pure properties, but allow one to document and analyse the ideal behaviour. In this paper we build upon a real-time deontic logic allowing the description of such contracts and present a runtime verification tool for monitoring of such contracts. We present a verification algorithm used to monitor contracts written in this logic and an airport agreement is used as a case study to illustrate how such agreements and contracts can be monitored using our tool with reasonable processing costs.
Sociosonic Interventions: Distributed Authorship in Socially Engaged Sound Practices

How do creative sound practices function in the context of socially engaged art? Toward developing a practical methodology, this paper focuses on sound-led projects that stage socially engaged art practice in community settings, including some involving the author. Aesthetics, ethics and politics are employed as interrogative lenses for distributed creative processes. Methods for collaborative art-making that facilitate a balance between these lenses are discussed, with the author further arguing the necessity of artistic “disruption.” Such sociosonic interventions are demonstrated to occur most effectively when sound practices challenge the paradigm of unidirectional audial reproduction: rupturing traditional hierarchies of creator and listener.
An Intelligent Chatbot Utilizing BERT Model and Knowledge Graph

As artificial intelligence is actively studied, it is being applied to various fields such as image, video and natural language processing. The natural language processing, in particular, is being studied to enable computers to understand the languages spoken and spoken by people and is considered one of the most important areas in artificial intelligence technology. In natural language processing, it is a complex, but important to make computers learn to understand a person’s common sense and generate results based on the person’s common sense. Knowledge graphs, which are linked using the relationship of words, have the advantage of being able to learn common sense easily from computers. However, the existing knowledge graphs are organized only by focusing on specific languages and fields and have limitations that cannot respond to neologisms. In this paper, we propose an intelligent chatbotsystem that collects and analyzed data in real time to build an automatically scalable knowledge graph and utilizes it as the base data. In particular, the fine-tuned BERT-based for relation extraction is to be applied to auto-growing graph to improve performance. And, we have developed a chatbot that can learn human common sense using auto-growing knowledge graph, it verifies the availability and performance of the knowledge graph.
Face Quality Assessment Based on Local Gradient

Face recognition system performance is affected by face image quality. In this paper, a face matching score, instead of a subjective perceptual score, is used as the image quality label to build a face image quality database; therefore, the ground truth quality achieved high accordance with the recognition performance. The database contains five common practical application distortion types for 19 people and three to five collection scenes for each. In addition, a face image quality algorithm based on a local gradient is proposed to judge the image quality. Making use of the fact that face landmarks can locate contour areas that have rich gradient information, a new mask based on human visual characteristics is designed to evaluate the image quality by using the local gradient information of landmark neighborhoods; this method was used to control for the influence of the image content and background. Our algorithm does not rely on a large amount of training data, and it has low computational complexity. The experimental results show that the algorithm is effective and efficient for evaluating image quality and improving the performance of face recognition systems.
Improving the Inference Accuracy of Diffractive Optical Neural Networks Using Class-specific Differential Detection

We report all-optical object classification systems that are based on class-specific design of diffractive neural networks followed by a differential detection scheme. The blind inference accuracies achieved through this framework are significantly enhanced.
Resource allocation in clusters of virtual machines

The problem addressed in this note refers to an important separation constraint that is commonly imposed on the assignment of virtual machines to physical servers. Namely, the requirements of high-availability and security of services, as well as design of database systems, usually specify classes of virtual machines that must be separated, that is, located on separate physical hosts. In the following sections we propose ways of allocating a given set of virtual machines on physical hosts that meet the requirements of separation while optimizing allocation of shared computing resources.
High negation questions and epistemic bias

High negation questions—questions like “Isn’t it nice out?”—necessarily convey that the speaker is epistemically  biased, i.e., the speaker has a prior belief about the correct answer to the question. In particular,  the speaker necessarily expresses bias toward the proposition embedded under the high negation.  Low negation questions, on the other hand, do not necessarily convey this bias. Romero and Han (2004) demonstrate that this asymmetry holds in several languages, some unrelated. This remarkable crosslinguistic  fact merits explanation. In particular, what role does the structural height of negation play in triggering this epistemic bias?  In this paper, I present novel evidence demonstrating that high negation questions lack propositional negation. This evidence is taken to support Ladd’s (1981) suggestion that high negation is somehow outside of the proposition,  and is used to motivate an analysis in which high negation scopes above an epistemic operator.  I argue that this analysis of high negation enables a novel account of epistemic bias that predicts its context insensitivity.
Time-Dependent State Prediction for the Kalman Filter Based on Recurrent Neural Networks

Traditional formulations of the well-established Kalman filter build upon prediction models which are linear and Gaussian, moreover they usually adopt the Markov property which excludes any form of long-term temporal dependencies. However, targets might follow specific behavioural patterns based on, e.g., their origin or destination, therefore time dependencies become highly relevant. In this article, the recently developed Mnemonic Kalman Filter is analysed which predicts the full Gaussian density of a target based on its previous position using a recurrent neural network with Long Short-Term Memory. For comparison, a simpler Long Short-Term Memory Kalman Filter is introduced which only provides a prediction of the target state vector. The presented experiments suggest that the learning-based approaches are highly relevant for time-dependent scenarios with low detection rates or possible occlusions. Furthermore, uncertainty estimation plays an important role in the filtering process.
Efficient Approximate Quantum State Tomography with Basis Dependent Neural-Networks.

We use a meta-learning neural-network approach to predict measurement outcomes of a quantum state in arbitrary local bases and thus carry out an approximate quantum state tomography. Each stage of this procedure can be performed efficiently, allowing it to be used effectively on large systems. We demonstrate this approach on the most recent noisy intermediate-scale IBM Quantum devices, achieving an accurate generative model for a 6-qubit state's measurement outcomes with only 100 random measurement settings as opposed to the 729 settings required for full tomography. This reduction in the required number of measurements scales favourably, with around 200 measurement settings yielding good results for a 10 qubit state that would require 59,049 settings for full quantum state tomography. This reduction in the number of measurement settings coupled with the efficiency of the procedure could allow for estimations of expectation values and state fidelities in practicable times on current quantum devices. For suitable states, this could then help in increasing the speed of other optimization schemes when attempting to produce states on noisy quantum devices at a scale where traditional maximum likelihood based approaches are impractical.
System Health Indicators in Mixed Criticality E/E Systems in Automated Driving Context

One problem standing in the way of fully automated vehicles is the question of how to ensure vehicle safety and the safety of all traffic participants. Standards like ISO 26262 and ISO/PAS 21448 tackle those issues from different viewpoints by defining safety measures and mechanisms. While ISO 26262 focuses on safety hazards arising from malfunctioning of E/E systems, ISO/PAS 21448 stresses hazards due to technological limitations. However, it is an open challenge how system-wide safety can be monitored and validated at run-time. To complement those safety specifications we propose a system-wide run-time safety analysis. Our System Health Management concept is based on so-called Health Indicators (HIs) to propagate knowledge about detected errors and trigger appropriate error reactions. We analyze probable information sources to define meaningful HIs in automated driving context and investigate influence factors, of both ISO 26262 and ISO/PAS 21448. We apply our approach to a case study demonstrating its applicability in an automated driving scenario.
A Comparative Study for Sentiment Analysis: LDA and LDA2Vec

Internet technology developed a new era of data mining by sharing their reviews, thoughts and ideas regarding any topic.Web 2.0 emerged as a new generation of Internet Services and created various social medium like Twitter, Facebook and creative blogs a data warehouse for exchanging and expressing their views.Nowadays enormous amount of data is accessible linked to any topic whether it will be Political agenda, Business enterprise, Survey organizations or different advertising firms.Customer contentment is the major component for various survey companies so as to improve their services.LDA2Vec is new approach proposed by Chris Moody and much research related to this approach is still not done.In this paper we used Latent Dirichet allocation (LDA) and LDA2Vec model for Sentiment classification.We evaluated performance of both the models by using corpus of 1000 records.After execution of model, we can easily demonstrate by our experimental results that hybrid approach of LDA2Vec (LDA and Word2Vec) performed better in comparison to LDA approach.
Quantitative analysis of heavy metal pollution in surface soil along the Funan River by XRF

Abstract With the development of urbanization and industrialization, the excessive use of metals leads to a sharp decrease in soil resources. Funan River runs through the whole urban area of Chengdu, and the surrounding population density is large, and environmental governance needs exist all the year round. It is of great significance to test the soil heavy metal content in this area. Under current environmental pollution, heavy metals mainly refer to biotoxic materials such as lead, cadmium, mercury and chromium, which are widely distributed in the atmosphere, water, soil and other substances in nature. They will react with proteins and enzymes in the human body, thus losing their original activities and even causing chronic poisoning. In this paper, reasonable sampling, sample treatment, X-ray fluorescence analysis and other methods were carried out for the surface soil along the Funan River, and the quantitative analysis of heavy metals was carried out. Results show that the heavy metal content of surface soil Chengdu Funan river coast are not more than the soil environment quality standard “regulation of the secondary standard, however, As, Cu and Zinc are found in the experiment of different degrees of pollution, including the As affected by natural factors, pollution is not obvious, the Cu and Zn pollution is more obvious, and concentrated in the industrial zone, highway interchange, namely industrial pollution and traffic pollution are more serious.