Reviewerly/exHarmony huggingface dataset:
Constraint Preserving Mixers for the Quantum Approximate Optimization Algorithm

The quantum approximate optimization algorithm/quantum alternating operator ansatz (QAOA) is a heuristic to find approximate solutions of combinatorial optimization problems. Most of the literature is limited to quadratic problems without constraints. However, many practically relevant optimization problems do have (hard) constraints that need to be fulfilled. In this article, we present a framework for constructing mixing operators that restrict the evolution to a subspace of the full Hilbert space given by these constraints. We generalize the “XY”-mixer designed to preserve the subspace of “one-hot” states to the general case of subspaces given by a number of computational basis states. We expose the underlying mathematical structure which reveals more of how mixers work and how one can minimize their cost in terms of the number of CX gates, particularly when Trotterization is taken into account. Our analysis also leads to valid Trotterizations for an “XY”-mixer with fewer CX gates than is known to date. In view of practical implementations, we also describe algorithms for efficient decomposition into basis gates. Several examples of more general cases are presented and analyzed.
A novel part-level feature extraction method for fine-grained vehicle recognition

In this paper, we propose a novel part-level feature extraction method to enhance the discriminative ability of deep convolutional features for the task of fine-grained vehicle recognition. Generally, the challenges for fine-grained vehicle recognition are mainly caused by the subtle visual differences between part regions of vehicles. Therefore, it is essential to extract discriminative features from part regions. Many existing methods, especially deep convolutional neural networks (D-CNNs), tend to detect the discriminative part regions explicitly or learn the part information implicitly through network restructuring and neglect the abundant part-level information contained in the high-level features generated by CNNs. In light of this, we propose a simple and effective part-level feature extraction method to enhance the representation of part-level features within the global features of target object generated by the backbone networks. The proposed method is built on the deep convolutional layers from which the discriminative part features could be integrated and extracted accordingly. More specifically, a basic feature grouping module is adopted to integrate the feature maps of deep convolutional layers into groups in each of which the related discriminative parts are assembled. The feature grouping process is performed in a multi-stage manner to ensure the integration process. Then a fusion module follows to model the coarse-to-fine relationship of the part features and further ensure the integrity and effectiveness of the part features. We conduct comparison experiments on public datasets, and the results show that the proposed method achieves comparable performance with state-of-the-art algorithms.
Monitoring, Recognition and Attendance Automation in Online Class: Combination of Image Processing, Cryptography in IoT Security

According to Oxford and Cambridge dictionary, the attested meaning of monitoring is an uninterrupted observation towards a particular circumstance for a specific period and inventing some new thing in it. In a word, we can tag it as “Supervision.” Regarding automation, the aphorism of both dictionaries is a work executed using self-operating machinery without any control of human beings. “Mechanization” is a substitution for the same. During the online class, continuous monitoring is essential and on the other hand, taking attendance is an obligatory task. It takes an adjunct effort and additional time involvement aside from the class hours. But if both these exigencies come under one umbrella with a very new aspect and a firm conviction, how will it be? IoT security and automation have collaborated to make this successful. This paper is an amalgamation of uninterrupted cognizance and guaranteed genuine automation on attendance marking. It contains the feature of data encryption using Fernet Cryptography to eschew manipulation. Another quality of this paper is that it shows a trail to detect human faces dexterously using Haar Cascade and Shape Predictor. The paper proposes a razor-sharp face authentication, discrepancy elimination and acts as a selectively permeable membrane. This paper provides a substantial replacement for manual attendance. The intention of generating this report is to bring ease to the online monitoring and attendance-taking system. The information presents its Promethean features with a minor error of 4% using Percentage error.
A Data-Driven Short-Term PV Generation and Load Forecasting Approach for Microgrid Applications

The data-driven (DD) is a systematic approach to improve the data and model by deriving/adding features to address the problem identified during the iterative loop of forecasting model development.This article proposes a DD framework for forecasting short-term PV generation and load demand.A framework of three stages with a unique contribution in each stage, such as generalizing data preprocessing steps (stage-1), multivariate feature generation and selection (stage-2), and model hyperparameter tuning (stage-3) for further improvement in forecasting is proposed.It focuses on data as well as forecasting models.The whole process is analyzed using the time-series measured data collected from a real-life demonstration project in Ireland.Data preprocessing is generalized for both generation and demand forecasting under the same framework.The relevant features are selected with the help of the proposed random forest sequential forward feature selection algorithm.Hyperparameters are tuned through tree-structured Parzen estimator algorithm for further improvement.In addition, the performance of the classical autoregressive integrated moving average model is compared with the machine learning-based gate recurrent unit, long short term memory, recurrent neural network, and convolutional neural network models.Results show that the data-driven forecasting model framework systematically improves the model performance.The seasonal variation has also a high impact on the model performances.
Multi-Objective Safe-Interval Path Planning With Dynamic Obstacles

Path planning among dynamic obstacles is a fundamental problem in Robotics with numerous applications. In this work, we investigate a problem called Multi-Objective Path Planning with Dynamic Obstacles (MOPPwDO), which requires finding collision-free Pareto-optimal paths amid obstacles moving along known trajectories while simultaneously optimizing multiple conflicting objectives, such as arrival time, communication robustness and obstacle clearance. Most of the existing multi-objective A*-like planners consider no dynamic obstacles, and naively applying them to address MOPPwDO can lead to large computation times. On the other hand, efficient algorithms such as Safe-Interval Path Planing (SIPP) can handle dynamic obstacles but for a single objective. In this work, we develop an algorithm called MO-SIPP by leveraging both the notion of safe intervals from SIPP to efficiently represent the search space in the presence of dynamic obstacles, and search techniques from multi-objective A* algorithms. We show that MO-SIPP is guaranteed to find the entire Pareto-optimal front, and verify MO-SIPP with extensive numerical tests with two and three objectives. The results show that the MO-SIPP runs up to an order of magnitude faster than the conventional alternates.
Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) is one of the most popular ML algorithms, and can be considered as an accurate algorithm in the process of classification, but has not been used in solving DR detection. Therefore, this work aims to apply the GWO-ELM classifier and employ one of the most popular features extractions, Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA), to increase the accuracy of DR detection system. Although the HOG-PCA has been tested in many image processing domains including medical domains, it has not yet been tested in DR. The GWO-ELM can prevent overfitting, solve multi and binary classifications problems, and it performs like a kernel-based Support Vector Machine with a Neural Network structure, whilst the HOG-PCA has the ability to extract the most relevant features with low dimensionality. Therefore, the combination of the GWO-ELM classifier and HOG-PCA features might produce an effective technique for DR classification and features extraction. The proposed GWO-ELM is evaluated based on two different datasets, namely APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD), in both binary and multi-class classification. The experiment results have shown an excellent performance of the proposed GWO-ELM model where it achieved an accuracy of 96.21% for multi-class and 99.47% for binary using APTOS-2019 dataset as well as 96.15% for multi-class and 99.04% for binary using IDRiD dataset. This demonstrates that the combination of the GWO-ELM and HOG-PCA is an effective classifier for detecting DR and might be applicable in solving other image data types.
A photovoltaic power prediction approach enhanced by feature engineering and stacked machine learning model

The rapid depletion of world reserves of fossil fuels escalates energy costs, raises concerns regarding energy supplies, and increases climate impacts. The deployment of renewable energy has therefore become a worldwide trend. Among the other known forms of energy, solar energy is unquestionably the cleanest. In an era of widespread deployment of solar photovoltaic plants, research and development activities have made significant progress in assessing and investigating the behavior of solar installations in order to increase their reliability; however, there is still a local component that has not been thoroughly considered in many climate areas, including the semi-arid one. Understanding the distinctive features of each environment as well as how they affect photovoltaic power is the first step towards developing intelligent and data-driven maintenance algorithms suited to the region’s environmental context. This study proposes a stacked machine learning model making hourly predictions of two PV systems varying in size and age. Three machine learning algorithms were compared with a baseline linear regression model and a reference physical model for the PV prediction. The algorithms were also evaluated in terms of the input predictors. The findings demonstrate that the stacked ensemble model outperforms the individual models chosen for comparison in both systems and offers new perspectives for improving PV prediction in this area.
Information and technology case as an indicator of information competence level of the translator

The article deals with the innovative approach to the organization of the information training of translators. The proposed approach will ensure not only the formation of information competence of future translators, but also the formation of an individual information and technology case of the translator. The components of an individual information and technology case are determined. They may include electronic terminology databases, translation memory databases for use in automated translation systems, databases of electronic links to terminological resources network, databases of electronic links to corpora of parallel texts. The using information and technology case of the translator as one of the diagnostic tools for evaluating the information competence level of the translator is proposed. It was found that the creating information and technology case is effective in developing information literacy and improving information technology skills.
The psychology of the internet fraud victimization of older adults: A systematic review

Criminals targeting and exploiting older adults in online environments are of great concern. This study systematically retrieved and analyzed articles on the psychological characteristics of older adult victims of online fraud. First, we found that there was no evidence that older adults were more prevalent than other individuals of other ages among online fraud victims, and current researchers have focused more on why older adults are easy targets for fraud (susceptibility to being cheated). Second, research on psychological factors of older adults' susceptibility to online fraud has mainly focused on cognitive function, trust traits, and other personality traits, such as social loneliness, the Big Five personality traits, and self-control. Among them, most researchers claim that the cyber-cheating of older adults may be due to a decline in their cognitive function. However, there has not been a consensus on how cognitive function and physical and mental conditions affect older people who are cheated. Third, techniques (i.e., methods and techniques used by fraudsters) and experience (i.e., familiarity with internet technology or fraud) may be related to the susceptibility of older adults to fraud, and these studies have also not yet generated a consensus supported by reliable data. Based on the above research uncertainties, we propose that fraud prevention and control strategies for older adults should be applied with caution.
Detection and moderation of detrimental content on social media platforms: current status and future directions

Social Media has become a vital component of every individual's life in society opening a preferred spectrum of virtual communication which provides an individual with a freedom to express their views and thoughts. While virtual communication through social media platforms is highly desirable and has become an inevitable component, the dark side of social media is observed in form of detrimental/objectionable content. The reported detrimental contents are fake news, rumors, hate speech, aggressive, and cyberbullying which raise up as a major concern in the society. Such detrimental content is affecting person’s mental health and also resulted in loss which cannot be always recovered. So, detecting and moderating such content is a prime need of time. All social media platforms including Facebook, Twitter, and YouTube have made huge investments and also framed policies to detect and moderate such detrimental content. It is of paramount importance in the first place to detect such content. After successful detection, it should be moderated. With an overflowing increase in detrimental content on social media platforms, the current manual method to identify such content will never be enough. Manual and semi-automated moderation methods have reported limited success. A fully automated detection and moderation is a need of time to come up with the alarming detrimental content on social media. Artificial Intelligence (AI) has reached across all sectors and provided solutions to almost all problems, social media content detection and moderation is not an exception. So, AI-based methods like Natural Language Processing (NLP) with Machine Learning (ML) algorithms and Deep Neural Networks is rigorously deployed for detection and moderation of detrimental content on social media platforms. While detection of such content has been receiving good attention in the research community, moderation has received less attention. This research study spans into three parts wherein the first part emphasizes on the methods to detect the detrimental components using NLP. The second section describes about methods to moderate such content. The third part summarizes all observations to provide identified research gaps, unreported problems and provide research directions.
Ubiq-exp: A toolkit to build and run remote and distributed mixed reality experiments

Developing mixed-reality (MR) experiments is a challenge as there is a wide variety of functionality to support. This challenge is exacerbated if the MR experiment is multi-user or if the experiment needs to be run out of the lab. We present Ubiq-Exp - a set of tools that provide a variety of functionality to facilitate distributed and remote MR experiments. We motivate our design and tools from recent practice in the field and a desire to build experiments that are easier to reproduce. Key features are the ability to support supervised and unsupervised experiments, and a variety of tools for the experimenter to facilitate operation and documentation of the experimental sessions. We illustrate the potential of the tools through three small-scale pilot experiments. Our tools and pilot experiments are released under a permissive open-source license to enable developers to appropriate and develop them further for their own needs.
Owl

This work documents our experience of improving the scheduler in Alibaba Function Compute, a public FaaS platform. It commences with our observation that memory and CPU are under-utilized in most FaaS sandboxes. A natural solution is to overcommit VM resources when allocating sandboxes, whereas the ensuing contention may cause performance degradation and compromise user experience. To complicate matters, the degradation in FaaS can arise from external factors, such as failed dependencies of user functions.
EPick: Attention-based multi-scale UNet for earthquake detection and seismic phase picking

Earthquake detection and seismic phase picking play a crucial role in the travel-time estimation of P and S waves, which is an important step in locating the hypocenter of an event. The phase-arrival time is usually picked manually. However, its capacity is restricted by available resources and time. Moreover, noisy seismic data present an additional challenge for fast and accurate phase picking. We propose a deep learning-based model, EPick, as a rapid and robust alternative for seismic event detection and phase picking. By incorporating the attention mechanism into UNet, EPick can address different levels of deep features, and the decoder can take full advantage of the multi-scale features learned from the encoder part to achieve precise phase picking. Experimental results demonstrate that EPick achieves 98.80% accuracy in earthquake detection over the STA/LTA with 80% accuracy, and for phase arrival time picking, EPick reduces the absolute mean errors of P- and S- phase picking from 0.072 s (AR picker) to 0.030 s and from 0.189 s (AR picker) to 0.083 s, respectively. The result of the model generalization test shows EPick’s robustness when tested on a different seismic dataset.
SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image

Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense covers largely prohibits its wider applications. While several recent works have attempted to address this issue, they either operate with sparse views (yet still, a few of them) or on simple objects/scenes. In this work, we consider a more ambitious task: training neural radiance field, over realistically complex visual scenes, by “looking only once”, i.e., using only a single view. To attain this goal, we present a Single View NeRF (SinNeRF) framework consisting of thoughtfully designed semantic and geometry regularizations. Specifically, SinNeRF constructs a semi-supervised learning process, where we introduce and propagate geometry pseudo labels and semantic pseudo labels to guide the progressive training process. Extensive experiments are conducted on complex scene benchmarks, including NeRF synthetic dataset, Local Light Field Fusion dataset, and DTU dataset. We show that even whouzt pre-training on multi-view datasets, SinNeRF can yield photo-realistic novel-view synthesis results. Under the single image setting, SinNeRF significantly outperforms the current state-of-the-art NeRF baselines in all cases. Project page: https://vita-group.github.io/SinNeRF/ .
Uncertainty-DTW for Time Series and Sequences

Dynamic Time Warping (DTW) is used for matching pairs of sequences and celebrated in applications such as forecasting the evolution of time series, clustering time series or even matching sequence pairs in few-shot action recognition. The transportation plan of DTW contains a set of paths; each path matches frames between two sequences under a varying degree of time warping, to account for varying temporal intra-class dynamics of actions. However, as DTW is the smallest distance among all paths, it may be affected by the feature uncertainty which varies across time steps/frames. Thus, in this paper, we propose to model the so-called aleatoric uncertainty of a differentiable (soft) version of DTW. To this end, we model the heteroscedastic aleatoric uncertainty of each path by the product of likelihoods from Normal distributions, each capturing variance of pair of frames. (The path distance is the sum of base distances between features of pairs of frames of the path.) The Maximum Likelihood Estimation (MLE) applied to a path yields two terms: (i) a sum of Euclidean distances weighted by the variance inverse, and (ii) a sum of log-variance regularization terms. Thus, our uncertainty-DTW is the smallest weighted path distance among all paths, and the regularization term (penalty for the high uncertainty) is the aggregate of log-variances along the path. The distance and the regularization term can be used in various objectives. We showcase forecasting the evolution of time series, estimating the Fréchet mean of time series, and supervised/unsupervised few-shot action recognition of the articulated human 3D body joints.
DearKD: Data-Efficient Early Knowledge Distillation for Vision Transformers

Transformers are successfully applied to computer vision due to their powerful modeling capacity with self-attention. However, the excellent performance of transformers heavily depends on enormous training images. Thus, a data-efficient transformer solution is urgently needed. In this work, we propose an early knowledge distillation framework, which is termed as DearKD, to improve the data efficiency required by transformers. Our DearKD is a two-stage framework that first distills the inductive biases from the early intermediate layers of a CNN and then gives the transformer full play by training without distillation. Further, our DearKD can be readily applied to the extreme data-free case where no real images are available. In this case, we propose a boundary-preserving intra-divergence loss based on DeepInversion to further close the performance gap against the full-data counterpart. Extensive experiments on ImageNet, partial ImageNet, data-free setting and other downstream tasks prove the superiority of DearKD over its baselines and state-of-the-art methods.
High-speed detection of 1550 nm single photons with superconducting nanowire detectors

Superconducting nanowire single-photon detectors are a key technology for quantum information and science due to their high efficiency, low timing jitter, and low dark counts. In this work, we present a detector for single 1550 nm photons with up to 78% detection efficiency, timing jitter below 50 ps FWHM, 158 counts/s dark count rate, as well as a maximum count rate of 1.5 giga-counts/s at 3 dB compression. The PEACOQ detector (Performance-Enhanced Array for Counting Optical Quanta) comprises a linear array of 32 straight superconducting niobium nitride nanowires that span the mode of an optical fiber. This design supports high count rates with minimal penalties for detection efficiency and timing jitter. We show how these trade-offs can be mitigated by implementing independent readout for each nanowire and by using a temporal walk correction technique to reduce count-rate dependent timing jitter. These detectors make quantum communication practical on a 10 GHz clock.