Reviewerly/exHarmony huggingface dataset:
Network Policy Enforcement With Commodity Multiqueue NICs for Multitenant Data Centers

Data centers are the fundamental component in the Internet of Things (IoT) system architecture. Data center servers where IoT services are co-located require hierarchical network policy enforcement to ensure fair bandwidth sharing among tenants and to prioritize latency-sensitive traffic within a tenant simultaneously. Meanwhile, emerging network interface cards (NICs) in servers make use of multiple hardware queues to drive increasing line rates. Unfortunately, multiqueue NICs make it hard to enforce hierarchical policies because the NIC packet scheduler dequeues packets in a static round-robin (RR) fashion for per-flow fairness. In this article, we enable hierarchical network policy enforcement with existing commodity multiqueue NICs. We design TONIC, a multiqueue NIC packet scheduling solution that approximates hierarchical packet scheduling by manipulating the packet dequeueing sequence of the NIC scheduler through dynamic packet enqueueing decisions. Specifically, TONIC leverages multiple hardware queues and the double-ended queue structure of qdiscs to express different tenant weights and application priorities without hardware modifications. We implement a TONIC prototype as a Linux kernel module and evaluate it on a testbed with commodity multiqueue NICs. Our experiment results show that TONIC can enforce hierarchical policies consisting of weighted fair sharing and traffic prioritization while maintaining robustness to various network conditions.
A Case Study in Anticipating Insider Vulnerabilities Using Psychological Profiling

This paper presents a case study conducted on a small software enterprise to further validate a previously developed theoretical framework that anticipates insider threats and vulnerabilities using psychological profiles. The subjects were asked to take standard personality tests and were later presented with a series of challenges to determine their insider profile. We compare the findings with the framework’s results, and we present a set of insights following the research and an investigation in the aftermath of the experiment.
A Systematic Literature Review of Augmented Reality Used in Language Learning

Augmented Reality has great potentials in education, as this technology may help increase students’ learning motivation and engage them in deep learning. Nevertheless, teachers have concerns about how to integrate the technology into their daily teaching. Moreover, the studies on AR in language learning are relatively immature compared to in other subjects. The present review study aimed to investigate how AR has been used in practical language learning. Instead of learning outcomes, this study focused on exploring the various AR-supported activities, the pedagogy theories and strategies that enabled language learning, and the constraints of teachers’ enactment. A total of 17 published studies with empirical data were analyzed. The findings unpacked Augmented Reality's affordances and enactment constraints in language teaching and learning. Suggestions and implications were provided for researchers, designers, and educational practitioners in the field.
The Science Library’s providing information support of the Russian Academy of Sciences

The role and place of science libraries during the transfer to digital science information and online user services is examined. The authors argue that in these circumstances of transfer to digital communication and Internet-based services and facilities, the peer-reviewed journals still maintain their role as a key publication format and promptly deliver research findings to researchers and experts. Open access has not been widely adopted in fundamental sciences: the scholarly information awareness system has to be financed by the government. The network of academic and research libraries make the part of this system. Based on the experience of the centralized library network (CLN) of the Russian Academy of Sciences Library for Natural Sciences (RAS LNS), the possibilities of the vast academic library network are explored. The structure of full-text collection of CLN RAS LNS is analyzed. This collection core is made primarily of the National Electronic Subscription materials comprising the world most established academic journals. With the Internet, these resources can be delivered directly to researcher’s workplace via the two channels under the National Electronic Subscription program, i. e. via his/her organizational subscription or via the library providing services to his/her organization. Both channels should be integrated in the system where the core resources are provided directly to the organization, and individual components demanded by individual researcher – via the library network. This system of science information provision has been for several years efficiently functioning at CLN RAS LNS. The need for similar system for the outside of National Electronic Subscription program is substantiated.
Spatiotemporal Interpolation of High Frequency Irradiance Data for Inverter Testing

A model for interpolating irradiance data in space and time is developed using cloud motion vectors. It is tested using two networks of photodiodes in Eastern Canada that measure irradiance at time scales of milliseconds and spatial scales of tens of meters. The model captures network-average variability with skill scores of up to 86% compared to nearest neighbor interpolation. We provide case studies showing how to use this model to study inverter response to rapid irradiance fluctuations. We also use AC power measurements from an inverter collocated with one of the photodiode networks to benchmark our approach and the wavelet variability model.
Pseudo-random sequences with non-maximal length realized according to the Galois scheme based on the primitive polynomial in degree

Abstract We consider homogeneous and nonhomogeneous pseudo-random sequences of non-maximal length formed by Galois generator based on characteristic polynomial in degree. We discovered periodic polynomial structures in degree in general form and on characteristic examples. We presented and applied the decimation method of periodic sequences, giving their decomposition on elementary M-sequences and constant components. We solved the tasks of analyzing of the diversity of generated sequences, which are organized and ordered from elements of direct and inverse M-sequences, and zero and one constants. We have obtained a general analytical relationship between decimation results and probabilistic properties. We discovered conditions for forming equal probability properties of the resulting sequences and determined the boundary relations of the generator parameters with the initial task of the minimum order of elementary M-sequences.
DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension

We tackle the problem of monocular 3D reconstruction of articulated objects like humans and animals. We contribute DensePose 3D, a method that can learn such reconstructions in a weakly supervised fashion from 2D image annotations only. This is in stark contrast with previous deformable reconstruction methods that use parametric models such as SMPL pre-trained on a large dataset of 3D object scans. Because it does not require 3D scans, DensePose 3D can be used for learning a wide range of articulated categories such as different animal species. The method learns, in an end-to-end fashion, a soft partition of a given category-specific 3D template mesh into rigid parts together with a monocular reconstruction network that predicts the part motions such that they reproject correctly onto 2D DensePose-like surface annotations of the object. The decomposition of the object into parts is regularized by expressing part assignments as a combination of the smooth eigenfunctions of the Laplace-Beltrami operator. We show significant improvements compared to state-of-the-art nonrigid structure-from-motion baselines on both synthetic and real data on categories of humans and animals.
QCalc: a tool to compute classical and quantum communication rates over free-space optical channels

Free-Space Optical (FSO) communication is a technology that enables ultra-high-rate data exchange and will be crucial in enabling secure Quantum Key Distribution (QKD) over long distances. However, FSO communication systems are comparatively complex technologies, comprising a variety of subsystems that all have to work together in a coordinated way. Hence, assessing the feasibility of establishing a (classical or quantum) link in a given communication scenario is a difficult task in itself. Here we introduce QCalc, a software tool that aims at easing and streamlining this process, allowing the user to keep track of all the relevant parameters in a link budget. This software computes the total transmissivity, and thus, the signal intensity observed at the receiver for any given parameters of the transceivers and the FSO communication channel. The estimated signal intensity can then be used to compute either the link budget in a classical communication scenario or the secure key rate of a QKD protocol.
Representing Knowledge Graphs with Gaussian Mixture Embedding

Knowledge Graph Embedding (KGE) has attracted more and more attention and has been widely used in downstream AI tasks. Some proposed models learn the embeddings of Knowledge Graph (KG) into a low-dimensional continuous vector space by optimizing a customized loss function. However, these methods either ignore the polysemy of entities/relations in KG, or cannot model the uncertainty of them. Therefore, we propose KG2GM (Knowledge Graph to Gaussian Mixture), a density-based KGE method, for modeling the polysemy and uncertainty of KG simultaneously. Each entity/relation in KG is represented by a Gaussian mixture probability distribution. Each Gaussian component of the mixed distribution represents one kind of semantics, where the mean vector denotes its space position, and the covariance matrix denotes its uncertainty. In this way we can model the polysemy and uncertainty of KG simultaneously. We employ symmetrized KL divergence and EL (Expected Likelihood) to measure the component similarities and make their probabilistic combination as the score function of the triplet. We conduct experiments on link prediction with two benchmark datasets (WN18RR and FB15k-237). The results show that our method can effectively model the polysemy and uncertainty of entities and relations in KG.
Proposal of Jawi CAPTCHA Using Digraphia Feature of the Malay Language

This paper proposes a new text-based CAPTCHA using Jawi script and Latin script, which are both used in the Malay language writing system. Many web sites have adopted CAPTCHA to prevent bots and other automated programs from malicious activities such as posting comment spam. Text-based CAPTCHA is the most common and earliest CAPTCHA. But as optical character recognition (OCR) technology has improved, the intensity of distortions that must be applied to a CAPTCHA for it to remain unrecognizable by OCR has increased. This has reached a point where humans are having difficulty recognizing CAPTCHA text. The idea of the proposed CAPTCHA is to generate two identical character strings, one written in Latin script and the other in Jawi script. Because some of the strings characters are hidden by obstacles, users need to combine both strings to solve this CAPTCHA. This idea uses the fact that most Jawi characters have one-to-one correspondence with Latin characters. A series of experiments was carried out to evaluated the performance of the proposed CAPTCHA. First, a computer program was developed with various software languages for the usability evaluation. The results showed that the average time to solve the CAPTCHA and the accuracy rates were acceptable compared with the indices reported in existing research. Next, two OCR programs were applied to the Jawi CAPTCHA, and it was demonstrated that they could not read the partially hidden Latin and Jawi strings. Lastly, we discussed the effectiveness of the proposed CAPTCHA by relating to the priming effect.
An Assessment of In-Field Irradiance Sensor Accuracy and Error Mitigation Techniques

Irradiance sensing accuracy is a significant factor in many applications of remote monitoring of PV systems, from detecting and diagnosing underperformance to tracking and benchmarking performance trends across a fleet. This paper provides an assessment of the accuracy of common in-field irradiance sensors. The accuracy measurement approach is based on comparing co-located sensors within a plant, since there is no true reference available in the field. Variability and typical bias magnitudes are derived statistically and compared with that of satellite-based irradiance sources. A typical bias of 0.8% is found for total insolation, and a typical RMSE of 1-2% is estimated for daily and monthly aggregation intervals. The associated errors for satellite-based irradiance, reported by satellite-based data providers, are 2-4 times higher. With this accuracy assessment approach, sensor accuracies can be measured on-line remotely and may be used to inform detection thresholds in fault detection algorithms for each specific system. Finally, techniques are presented to mitigate sensor error when multiple sensors exist by finding and remove errors and by averaging the remaining data.
Foggy Image Detection Based on DehazeNet with improved SSD

In order to improve the ability of pedestrian detection in foggy scenes, a method is proposed to improve the performance of pedestrian detection in foggy scenes. DehazeNet convolution is combined with the improved SSD target detection algorithm to realize vehicle and pedestrian detection in foggy scene. Target detection model training was carried out by using the fog images after fog removal treatment and the original fog images, and vehicle and pedestrian detection was carried out in traffic environment with different fog concentration levels. The results showed that the mAP value of DehazeNet with SSD network could reach 79.7%, 5.4% higher than the mAP value of SSD algorithm.
Interactive effects of institutional requirements and screen vs. Print platforms on preference of Times New Roman and Calibri among university students

The replacement of Times New Roman (TNR—serif) with Calibri (sans serif) as default typeface on Microsoft Office Word reflects the growing prevalence of screen-based reading. The change also points to a festering conflict in the literature on the readability and legibility of serif and sans serif typefaces in print and computer-based platforms. The study examines preference of TNR and Calibri among Nigerian students whose universities prescribe TNR for all academic works. Studies show that learning institutions seldom consider the effects of typefaces on learners who also rarely contribute to institutional decisions on typeface choice. The study focuses on influence of text platform (screen or print) on reading speed, the effects of familiarity with typeface, and perceived importance of institutional requirements on typeface preference. Based on significant influences of text platform on reading speed and typeface preference, we make new propositions on how screen-based reading and type size may introduce new perspectives in perceptions of serifs as visual stimuli. We conclude therefore that the introduction of Calibri may resolve the persisting theoretical and empirical differences on the legibility and readability of serif and sans serif typefaces.
Acquisition of Cooperative Behavior in a Soccer Task Using Reward Shaping

In this research, soccer task is investigated among the numerous tasks of deep reinforcement learning. The soccer task requires cooperative behavior. However, it is difficult for the agents to acquire the behavior, because a reward is sparsely given. Moreover, the behaviors of the allies and opponents must be considered by the agents. In addition, in the soccer task, if the agents attempt to acquire high-level cooperative behavior from low-level movements, such as ball kicking, a huge amount of time will be needed to learn a model. In this research, we conduct experiments in which reward shaping is incorporated into deep reinforcement learning. This enables the agents to efficiently acquire cooperative behavior from low-level movements in a soccer task. The findings of this research indicate that reward shaping with a designer's domain knowledge positively influences the agent's attempt to acquire cooperative behavior from low-level movements.
Feature Extraction of Face Recognition Techniques Utilizing Neural System as a Classifier

Face recognition has a large extent of applications from individual recognizable proof and reconnaissance to electronics showcasing and publicizing for chosen clients. There are various advances in facial recognition, for example, pre-processing, feature extraction, and grouping, where feature extraction and grouping are utilized to acquire the greatest precision. In this paper, diverse feature extraction methods, for example, A.A.M, A.S.M, template-based, Gabor-features, and a few are basically surveyed. Aside from these, the various kinds of neural classification systems, for example, backpropagation, convolutional, radial-basis-function, and so on in the space of face recognition, are investigated. The method and calculations created in the present writing are examined, and it is uncovered that every system is one of a kind and has ideal execution. This assessment further makes a relative examination of these frameworks reliant on their focal points and imperatives.
Analyzing Regional and Local Changes in Irradiance during the 2019 Total Solar Eclipse in Chile, Using Field Observations and Analytical Modeling

Solar eclipses are astronomic phenomena in which the Earth’s moon transits between the planet and the Sun, projecting a shadow onto the planet’s surface. As solar power installed capacity increases, detailed studies of this region-wide phenomenon’s effect in irradiance is of interest; however, the literature mainly reports its effects on localized scales. A measurement campaign spanning over 1400 km was pursued for the 2 July 2019 total solar eclipse in Chile, to register the event and establish a modeling framework to assess solar eclipse effects in irradiance over wide regional scales. This work describes the event and presents an estimation framework to decompose atmospheric and eclipse effects on irradiance. An analytical model was applied to study irradiance attenuation throughout the Chilean mainland territory, using satellite-derived and astronomical data as inputs compared to ground measurements in eight stations. Results showed good agreement between model and observations, with Mean Bias Errors of −0.008 to 0.98 W/m2 for Global Horizontal Irradiance and −0.004 to −4.664 W/m2 for Direct Normal Irradiance, with Normalized Root Mean Squared Errors of 0.7–5.8% and 1.4–12.2%, respectively. Energy losses due to obscuration corresponded between 20–40% for Global Horizontal Irradiance and 25–50% for Direct Normal Irradiance over Chilean territory.
A Super Baseline for Pedestrian Re-Identification

In this paper, we propose a new baseline that combines and improves two types of deep learning algorithms to achieve more accurate pedestrian re-identification from images taken by surveillance cameras. We introduce some tricks into a famous strong baseline [1]. The main contributions are: 1) We improve IBN-Net [2] to replace the original ResNet50 (last stride = 1). 2) We perform model learning by using three common databases with different characteristics at the same time, except for the operation part of random erasing augmentation. 3) We add two new classifiers. 4) We optimize the parameters to prevent overfitting. We conducted ablation experiments on each trick using unlearned data and confirmed the effectiveness and stability of the proposed method from the results.
Pose-varied swing arm profilometer test for off-axis aspheric surfaces with strong asphericity

The swing arm profilometer (SAP) has been widely used to test large aspheric optics by measuring the asphericity from its best-fitting sphere (BFS). To further improve the test accuracy, we propose a pose-varied test mode for the SAP with a shorter-range probe to measure off-axis aspheric surfaces with stronger asphericity. In contrast to the classical SAP mode in which the air-table is fixed in a stationary position during measurement, we adjust the pose of each scan arc to match the local BFS and the measurement range of the probe decreases to half that of the global asphericity. To verify the effectiveness, we conduct experiments on an off-axis asphere with a diameter of 3 and 2 m. Compared with a classical SAP mode, it achieved an improved performance of 50% higher repeatability and 32% higher accuracy.
Chinese Named Entity Recognition Based on Bi-directional Quasi-Recurrent Neural Networks improved with BERT

Named Entity Recognition is a basic and important task in the field of natural language processing. In the field of Chinese NER, the BiLSTM-CRF model has always been favored by many researchers due to its excellent information extraction capabilities. However, with the advent of the big data era, LSTM cannot be calculated in parallel, exposing the shortcomings of low training efficiency. In this paper, we focused on model training efficiency, using Bi-directional Quasi-Recurrent Neural Networks (BiQRNN) to replace BiLSTM, the experimental results show that the model we proposed achieves good results and increases the training speed of the model by 35%.
Additive Attention for CNN-based Classification

Attention has been proved effective in many Computer Vision region. Squeeze and Excitation Network is a classic attention module in CNN, and in many regions, it has been proved effective. But some problems remain in SE-Net. We find that weight parameters of Full Connect layers in a trained SE-N et are not very important, compared to biases. Based on this observation, we proposed a light attention module named Additive Attention module, reducing the computation cost while achieving a similar performance with SE-ResNet-18.
Multi-task Learning for Newspaper Image Segmentation and Baseline Detection Using Attention-Based U-Net Architecture

In this work, we propose an end-to-end language agnostic multi-task learning based U-Net framework for performing text block segmentation and baseline detection in document images. We leverage the performance of U-Net by augmenting attention layers between the contracting and expansive path via skip connections. The generalization ability of the model is validated on handwritten images as well. We perform exhaustive experiments on ICPR2020 challenge dataset and obtain a test accuracy of 96.09% and 99.44% for simple track baseline detection and text block segmentation respectively, 97.47% and 98.51% complex track baseline and text block segmentation respectively. The source code is made publicly available at https://github.com/divyanshjoshi/Attention-U-Net-Newspaper-Text-Block-Segmentation.