Reviewerly/exHarmony huggingface dataset:
Quantum computing: prospects and challenges

The field of quantum computing has evolved into a large interdisciplinary community where significant resources are invested worldwide. As a result, accelerated progress is achieved and we are at the beginning of a new age of computation, developing programmable quantum systems towards universal quantum computers. Quantum computers promise to solve certain mathematical problems that are intractable to classical computers. Quantum computing systems are built from the bottom up reaching the limits of what can be classically simulated. The IBM Quantum Development Roadmap describes our vision of creating a quantum computing ecosystem delivering quantum applications through the cloud. This requires developing the entire quantum computing stack starting from the qubit and quantum processor technology, control electronics to software, algorithms and applications for quantum computing, implemented in the cloud and integrated with high performance computing. In this presentation the recent developments of our quantum computing systems and the scientific advances that enabled scaling superconducting quantum processors to 127 qubits are presented. Besides scale, also quality and speed will be discussed building the key metric for measuring the performance of quantum computation. Examples of applications where the computational power of quantum computing could make a difference are provided.
American Philosophy in TranslationNaokoSaitoRowan &amp; Littlefield International, 2019, Pp. 180.

Educational TheoryVolume 72, Issue 2 p. 243-252 BOOK REVIEW American Philosophy in Translation Naoko Saito Rowan & Littlefield International, 2019, Pp. 180. Reviewed byViktor Johansson, Corresponding Author Viktor Johansson viktor.johansson@sh.se Södertörn University, Sweden*Viktor Johansson, viktor.johansson@sh.seSearch for more papers by this author Reviewed byViktor Johansson, Corresponding Author Viktor Johansson viktor.johansson@sh.se Södertörn University, Sweden*Viktor Johansson, viktor.johansson@sh.seSearch for more papers by this author First published: 21 July 2022 https://doi.org/10.1111/edth.12525Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume72, Issue2April 2022Pages 243-252 RelatedInformation
A New Academic Conference Information Management System Based on Social Network

SCHOLAT is a free, massive and comprehensive academic social network platform, which aims to realize reliable data sharing. In this paper, a new conference information management system based on SCHOLAT is designed and developed. In addition to containing typical functions of conference information management system, the system also provides individual services, including convenient online conference space, personalized domain, association with SCHOLAT ecosystem and customized access control. A hybrid classification algorithm for SCHOLAT user’s academic emails is proposed to enhance the high quality services of the system. Benefit from convenient and helpful services, the conference system has grown rapidly in popularity since it was deployed and has been applied to lots of academic conferences up to now.
Towards task-free privacy-preserving data collection

With the rapid developments of Internet of Things (IoT) and proliferation of embedded devices, large volume of personal data are collected, which however, might carry massive private information about attributes that users do not want to share. Many privacy-preserving methods have been proposed to prevent privacy leakage by perturbing raw data or extracting task-oriented features at local devices. Unfortunately, they would suffer from significant privacy leakage and accuracy drop when applied to other tasks as they are designed and optimized for predefined tasks. In this paper, we propose a novel task-free privacy-preserving data collection method via adversarial representation learning, called TF-ARL, to protect private attributes specified by users while maintaining data utility for unknown downstream tasks. To this end, we first propose a privacy adversarial learning mechanism (PAL) to protect private attributes by optimizing the feature extractor to maximize the adversary's prediction uncertainty on private attributes, and then design a conditional decoding mechanism (ConDec) to maintain data utility for downstream tasks by minimizing the conditional reconstruction error from the sanitized features. With the joint learning of PAL and ConDec, we can learn a privacy-aware feature extractor where the sanitized features maintain the discriminative information except privacy. Extensive experimental results on real-world datasets demonstrate the effectiveness of TF-ARL.
Effects of Phonological Features on Texting Styles: Future Directions

This paper aims to investigate the effect of phonological features on online written text messages. Data were collected from ninety ESL Indian students at Aligarh Muslim University with regards to their Facebook-based chats. The dataset was fed into SPSS for analysis. Major findings showed that the respondents tend to use vowel deletion, the substitution of numbers for phones, words represented by a single letter and overuse of sounds to represent phonology in their written text messages. With this in mind, the study yielded evidence that texting reverses the previous studies that spelling first and then phonology. That is to say, this study revealed that phonology affects the way of writing online text messages. This study also showed that phonology is used in the written forms and that Facebook users sometimes reject to follow the standard spelling of English and write the words as they are pronounced, not as they are written in standard English. Keywords: internet linguistics, netSpeak, phonetic spelling, phonological features, textSpeak, Webspeak
Research on Model and Algorithm of Multiview and Multilabel Classification Based on Nearest-Neighbor Model

In the field of machine learning, multilabel learning gradually evolved from the traditional text classification problem. In this study, a multiview multilabel via optimal classifier chain (MVMLOCC) algorithm based on the nearest-neighbor model is proposed. The algorithm model establishes a multilabel chain classifier for each view of the dataset and predicts unknown data samples by dynamically adjusting the weights of chain learners. When an unknown example is input, multiple chain classifier models are multiplied by corresponding weights to obtain the final tag set situation. The model makes full use of the relevance between multiple tags and the complementarity and integrity between multiple perspectives and can achieve better learning results. The final experimental results show that our proposed model can be applied to different multilabel classification tasks and has achieved excellent performance under different evaluation indicators. This study mainly studies the multiview and multilabel classification method and uses active learning technology to solve the problem of high label components in data collection.
A Brief Overview of NASA's Newly-Instituted Planetary Data Ecosystem.&amp;#160;

&amp;lt;p&amp;gt;I will give a brief overview of NASA's Planetary Data Ecosystem (PDE) Independent Review Board's activities and Report and will discuss the Findings and Recommendations related to international collaborations for improving accessibility of data from archiving and preservation, searching and acquiring, and using NASA's Planetary Data.&amp;amp;#160;&amp;lt;/p&amp;gt;
Face Recognitiom At Varying angles

Face recognition is process of detecting facial images in real time and identifying facial image. Detecting and recognizing persons face to authenticate by their Multiview angled face is a real valued problem in machine vision. Multiview faces are having difficulties due to non-linear representation in the feature space. Facial images in surveillance or cellular scenarios often have large view-point variations in terms of pitch and yaw angles. This makes facial recognition more challenging. The main objective of this project is to Build a face recognition system which can identify facial images at different angles. Identifying suspects whose faces may be partially visible due to the varying angles at which CCTV cameras are typically placed. Identifying suspects by analysing CCTV feeds even when frontal face has not been clearly captured is the key challenge to this problem statement. This face recognition model is able to identify both frontal face and profile face this problem can be achieved using support vector machine (SVM) algorithm.
An Optimal Fuzzy Logic Controller Design for a Single-Linked Inverted Pendulum System

Inverted pendulum (IP) is an underactuated systems, since the input of the system is the force applied to the cart and the outputs are the cart position and pendulum angle (single input multi output - SIMO) system, which makes this system is highly nonlinear and unstable. Inverted pendulum considered as the one the most famous classical systems in the field of control and mechatronics. This work focuses on the design of an optimized fuzzy controller to stabilize an inverted pendulum in a vertical position. A continuous correction mechanism is required to move the cart in a certain way in order to balance the pendulum to prevent it from falling down. This project started by a derivation of the mathematical model of the single linked inverted pendulum system by using Euler-Lagrange method. After that, a fuzzy logic controller (FLC) based on Sugeno inference system was designed, and genetic algorithm was used to tune the parameters of the controller using MATLAB software. Both controllers were tested using real time inverted pendulum. Experimental results showed that in T-K Sugeno FLC, the pendulum stabilized after 4.5 s with overshoot equal to 0.029 rad. On the other hand, the cart reached steady state at 4.3 s and steady state error was 0.04 m. Optimized FLC achieved better results with the Sugeno, since the pendulum stabilized after 1.6 s, and the cart took only 1.34 s to reach the steady state.
Fitting Circles and Ellipses

AbstractThis chapter addresses the problem of fitting circles to a given set of data points in 2D, which arises in numerous applications in engineering, medicine, physics, remote sensing, archeology, etc. Despite the classic nature of this problem, reliable and efficient algorithms have only been developed in the last few decades and are still a topic of active research. As with straight lines (see Ch. 10) the goal is to find an optimal orthogonal fit of the point set in the least-squares sense. We distinguish two main types of fits: geometric fits minimize Euclidean orthogonal distances, while algebraic fits minimize some other (typically algebraic) expression as an approximation.
economic consequences of rapid population growth in Nigeria

Nigeria is currently the world's sixth most populous country. However, empirical research on the impact of this growing population on economic growth is divided. This study adds to existing research on the economic effects of Nigeria's rapid population growth. This study used a qualitative research design because it relied on secondary data gathered from books, journal papers, and online sources. Its content was examined in light of the objectives of the study, and Malthusian theory serves as the study's foundation. The majority of Nigeria's development issues, according to the study's findings, can be directly or indirectly linked to the country's rapid population growth. This study thus urges, among other things, the need for an all-encompassing mechanism that will address issues of conflict and insecurity, migration and displacement, access to education for girls, and proper regulation of infrastructure development, as well as the undiluted conservative ideology whereby many, especially in Nigeria, see population growth as the key to control of political power and resources that should be thrown away in favor of a more effective and functional system.
Cybersickness-aware Tile-based Adaptive 360{\deg} Video Streaming

In contrast to traditional videos, the imaging in virtual reality (VR) is 360{\deg}, and it consumes larger bandwidth to transmit video contents. To reduce bandwidth consumption, tile-based streaming has been proposed to deliver the focused part of the video, instead of the whole one. On the other hand, the techniques to alleviate cybersickness, which is akin to motion sickness and happens when using digital displays, have not been jointly explored with the tile selection in VR. In this paper, we investigate Tile Selection with Cybersickness Control (TSCC) in an adaptive 360{\deg} video streaming system with cybersickness alleviation. We propose an m-competitive online algorithm with Cybersickness Indicator (CI) and Video Loss Indicator (VLI) to evaluate instant cybersickness and the total loss of video quality. Moreover, the algorithm exploits Sickness Migration Indicator (SMI) to evaluate the cybersickness accumulated over time and the increase of optical flow to improve the tile quality assignment. Simulations with a real network dataset show that our algorithm outperforms the baselines regarding video quality and cybersickness accumulation.
Learning to Downsample for Segmentation of Ultra-High Resolution Images

Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget. Common solutions uniformly downsample the input images to meet memory constraints, assuming all pixels are equally informative. In this work, we demonstrate that this assumption can harm the segmentation performance because the segmentation difficulty varies spatially. We combat this problem by introducing a learnable downsampling module, which can be optimised together with the given segmentation model in an end-to-end fashion. We formulate the problem of training such downsampling module as optimisation of sampling density distributions over the input images given their low-resolution views. To defend against degenerate solutions (e.g. over-sampling trivial regions like the backgrounds), we propose a regularisation term that encourages the sampling locations to concentrate around the object boundaries. We find the downsampling module learns to sample more densely at difficult locations, thereby improving the segmentation performance. Our experiments on benchmarks of high-resolution street view, aerial and medical images demonstrate substantial improvements in terms of efficiency-and-accuracy trade-off compared to both uniform downsampling and two recent advanced downsampling techniques.
Erasing the which-path information of photons

Which-path information of a quantum particle in interferometers is the key to infer the past of quantum particle. It arises many extensive discussions including quantum complementarity and path-visibility relation. The basic of these discussions are the description, detection and control of which-path information. In this article, we focus on the investigation of multidimensional which-path information in nested Mach-Zehnder interferometer. A general expression of which-path information is given and can be partially extracted by different detection method. Further analysis shows that the which-path information can be controlled by the phase differences and beam splitting ratios between the arms of nested Mach-Zehnder interferometer. Moreover, a new which-path information elimination phenomenon has been predicted and demonstrated experimentally. Our work can help to understand the physics of quantum particles, potentially apply to quantum information process and quantum metrology.
AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection

Existing deep learning-based approaches for monocular 3D object detection in autonomous driving often model the object as a rotated 3D cuboid while the object's geometric shape has been ignored. In this work, we propose an approach for incorporating the shape-aware 2D/3D constraints into the 3D detection framework. Specifically, we employ the deep neural network to learn distinguished 2D keypoints in the 2D image domain and regress their corresponding 3D coordinates in the local 3D object coordinate first. Then the 2D/3D geometric constraints are built by these correspondences for each object to boost the detection performance. For generating the ground truth of 2D/3D keypoints, an automatic model-fitting approach has been proposed by fitting the deformed 3D object model and the object mask in the 2D image. The proposed framework has been verified on the public KITTI dataset and the experimental results demonstrate that by using additional geometrical constraints the detection performance has been significantly improved as compared to the baseline method. More importantly, the proposed framework achieves state-of-the-art performance with real time. Data and code will be available at https://github.com/zongdai/AutoShape
Custom-Tailored Clone Detection for IEC 61131-3 Programming Languages

Automated production systems (aPS) are highly customized systems that consist of hardware and software. Such aPS are controlled by a programmable logic controller (PLC), often in accordance with the IEC 61131-3 standard that divides system implementation into so-called program organization units (POUs) as the smallest software unit and is comprised of multiple textual and graphical programming languages that can be arbitrarily nested. A common practice during the development of such systems is reusing implementation artifacts by copying, pasting, and then modifying code. This approach is referred to as code cloning. It is used on a fine-granular level where a POU is cloned within a system variant. It is also applied on the coarse-granular system level, where the entire system is cloned and adapted to create a system variant, for example for another customer. This ad hoc practice for the development of variants is commonly referred to as clone-and-own. It allows the fast development of variants to meet varying customer requirements or altered regulatory guidelines. However, clone-and-own is a non-sustainable approach and does not scale with an increasing number of variants. It has a detrimental effect on the overall quality of a software system, such as the propagation of bugs to other variants, which harms maintenance. In order to support the effective development and maintenance of such systems, a detailed code clone analysis is required. On the one hand, an analysis of code clones within a variant (i.e., clone detection in the classical sense) supports experts in refactoring respective code into library components. On the other hand, an analysis of commonalities and differences between cloned variants (i.e., variability analysis) supports the maintenance and further reuse and facilitates the migration of variants into a software product line (SPL).
Multi-Expert Adversarial Attack Detection in Person Re-identification  Using Context Inconsistency

The success of deep neural networks (DNNs) haspromoted the widespread applications of person re-identification (ReID). However, ReID systems inherit thevulnerability of DNNs to malicious attacks of visually in-conspicuous adversarial perturbations. Detection of adver-sarial attacks is, therefore, a fundamental requirement forrobust ReID systems. In this work, we propose a Multi-Expert Adversarial Attack Detection (MEAAD) approach toachieve this goal by checking context inconsistency, whichis suitable for any DNN-based ReID systems. Specifically,three kinds of context inconsistencies caused by adversar-ial attacks are employed to learn a detector for distinguish-ing the perturbed examples, i.e., a) the embedding distancesbetween a perturbed query person image and its top-K re-trievals are generally larger than those between a benignquery image and its top-K retrievals, b) the embedding dis-tances among the top-K retrievals of a perturbed query im-age are larger than those of a benign query image, c) thetop-K retrievals of a benign query image obtained with mul-tiple expert ReID models tend to be consistent, which isnot preserved when attacks are present. Extensive exper-iments on the Market1501 and DukeMTMC-ReID datasetsshow that, as the first adversarial attack detection approachfor ReID,MEAADeffectively detects various adversarial at-tacks and achieves high ROC-AUC (over 97.5%).
Optimizing Latency for Online Video CaptioningUsing Audio-Visual  Transformers

Video captioning is an essential technology to understand scenes and describe events in natural language. To apply it to real-time monitoring, a system needs not only to describe events accurately but also to produce the captions as soon as possible. Low-latency captioning is needed to realize such functionality, but this research area for online video captioning has not been pursued yet. This paper proposes a novel approach to optimize each caption's output timing based on a trade-off between latency and caption quality. An audio-visual Trans-former is trained to generate ground-truth captions using only a small portion of all video frames, and to mimic outputs of a pre-trained Transformer to which all the frames are given. A CNN-based timing detector is also trained to detect a proper output timing, where the captions generated by the two Trans-formers become sufficiently close to each other. With the jointly trained Transformer and timing detector, a caption can be generated in the early stages of an event-triggered video clip, as soon as an event happens or when it can be forecasted. Experiments with the ActivityNet Captions dataset show that our approach achieves 94% of the caption quality of the upper bound given by the pre-trained Transformer using the entire video clips, using only 28% of frames from the beginning.
Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech  Attacks

Existing approaches for anti-spoofing in automatic speaker verification (ASV) still lack generalizability to unseen attacks. The Res2Net approach designs a residual-like connection between feature groups within one block, which increases the possible receptive fields and improves the system's detection generalizability. However, such a residual-like connection is performed by a direct addition between feature groups without channel-wise priority. We argue that the information across channels may not contribute to spoofing cues equally, and the less relevant channels are expected to be suppressed before adding onto the next feature group, so that the system can generalize better to unseen attacks. This argument motivates the current work that presents a novel, channel-wise gated Res2Net (CG-Res2Net), which modifies Res2Net to enable a channel-wise gating mechanism in the connection between feature groups. This gating mechanism dynamically selects channel-wise features based on the input, to suppress the less relevant channels and enhance the detection generalizability. Three gating mechanisms with different structures are proposed and integrated into Res2Net. Experimental results conducted on ASVspoof 2019 logical access (LA) demonstrate that the proposed CG-Res2Net significantly outperforms Res2Net on both the overall LA evaluation set and individual difficult unseen attacks, which also outperforms other state-of-the-art single systems, depicting the effectiveness of our method.