Reviewerly/exHarmony huggingface dataset:
Realization of a quantum perceptron gate with trapped ions

We report the implementation of a perceptron quantum gate in an ion-trap quantum computer. In this scheme, a perceptron's target qubit changes its state depending on the interactions with several qubits. The target qubit displays a tunable sigmoid switching behaviour becoming a universal approximator when nested with other percetrons. The procedure consists on the adiabatic ramp-down of a dressing-field applied to the target qubit. We also use two successive perceptron quantum gates to implement a XNOR-gate, where the perceptron qubit changes its state only when the parity of two input qubits is even. The applicability can be generalized to higher-dimensional gates as well as the reconstruction of arbitrary bounded continuous functions of the perceptron observables.
Dynamic multi feature-class Gaussian process models

In model-based medical image analysis, three features of interest are the shape of structures of interest, their relative pose, and image intensity profiles representative of some physical property. Often, these are modelled separately through statistical models by decomposing the object's features into a set of basis functions through principal geodesic analysis or principal component analysis. This study presents a statistical modelling method for automatic learning of shape, pose and intensity features in medical images which we call the Dynamic multi feature-class Gaussian process models (DMFC-GPM). A DMFC-GPM is a Gaussian process (GP)-based model with a shared latent space that encodes linear and non-linear variation. Our method is defined in a continuous domain with a principled way to represent shape, pose and intensity feature classes in a linear space, based on deformation fields. A deformation field-based metric is adapted in the method for modelling shape and intensity feature variation as well as for comparing rigid transformations (pose). Moreover, DMFC-GPMs inherit properties intrinsic to GPs including marginalisation and regression. Furthermore, they allow for adding additional pose feature variability on top of those obtained from the image acquisition process; what we term as permutation modelling. For image analysis tasks using DMFC-GPMs, we adapt Metropolis-Hastings algorithms making the prediction of features fully probabilistic. We validate the method using controlled synthetic data and we perform experiments on bone structures from CT images of the shoulder to illustrate the efficacy of the model for pose and shape feature prediction. The model performance results suggest that this new modelling paradigm is robust, accurate, accessible, and has potential applications including the management of musculoskeletal disorders and clinical decision making
BDFA: A Blind Data Adversarial Bit-flip Attack on Deep Neural Networks

Adversarial bit-flip attack (BFA) on Neural Network weights can result in catastrophic accuracy degradation by flipping a very small number of bits. A major drawback of prior bit flip attack techniques is their reliance on test data. This is frequently not possible for applications that contain sensitive or proprietary data. In this paper, we propose Blind Data Adversarial Bit-flip Attack (BDFA), a novel technique to enable BFA without any access to the training or testing data. This is achieved by optimizing for a synthetic dataset, which is engineered to match the statistics of batch normalization across different layers of the network and the targeted label. Experimental results show that BDFA could decrease the accuracy of ResNet50 significantly from 75.96\% to 13.94\% with only 4 bits flips.
Using Convolutional Neural Networks to Detect Compression Algorithms

Machine learning is penetrating various domains virtually, thereby proliferating excellent results. It has also found an outlet in digital forensics, wherein it is becoming the prime driver of computational efficiency. A prominent feature that exhibits the effectiveness of ML algorithms is feature extraction that can be instrumental in the applications for digital forensics. Convolutional Neural Networks are further used to identify parts of the file. To this end, we observed that the literature does not include sufficient information about the identification of the algorithms used to compress file fragments. With this research, we attempt to address this gap as compression algorithms are beneficial in generating higher entropy comparatively as they make the data more compact. We used a base dataset, compressed every file with various algorithms, and designed a model based on that. The used model was accurately able to identify files compressed using compress, lzip and bzip2.
DIY Graphics Tab: A Cost-Effective Alternative to Graphics Tablet for  Educators

Everyday, more and more people are turning to online learning, which has altered our traditional classroom method. Recording lectures has always been a normal task for online educators, and it has lately become even more important during the epidemic because actual lessons are still being postponed in several countries. When recording lectures, a graphics tablet is a great substitute for a whiteboard because of its portability and ability to interface with computers. This graphic tablet, however, is too expensive for the majority of instructors. In this paper, we propose a computer vision-based alternative to the graphics tablet for instructors and educators, which functions largely in the same way as a graphic tablet but just requires a pen, paper, and a laptop's webcam. We call it "Do-It-Yourself Graphics Tab" or "DIY Graphics Tab". Our system receives a sequence of images of a person's writing on paper acquired by a camera as input and outputs the screen containing the contents of the writing from the paper. The task is not straightforward since there are many obstacles such as occlusion due to the person's hand, random movement of the paper, poor lighting condition, perspective distortion due to the angle of view, etc. A pipeline is used to route the input recording through our system, which conducts instance segmentation and preprocessing before generating the appropriate output. We also conducted user experience evaluations from the teachers and students, and their responses are examined in this paper.
Panoptic-based Object Style-Align for Image-to-Image Translation

Despite remarkable recent progress in image translation, the complex scene with multiple discrepant objects remains a challenging problem. Because the translated images have low fidelity and tiny objects in fewer details and obtain unsatisfactory performance in object recognition. Without the thorough object perception (i.e., bounding boxes, categories, and masks) of the image as prior knowledge, the style transformation of each object will be difficult to track in the image translation process. We propose panoptic-based object style-align generative adversarial networks (POSA-GANs) for image-to-image translation together with a compact panoptic segmentation dataset. The panoptic segmentation model is utilized to extract panoptic-level perception (i.e., overlap-removed foreground object instances and background semantic regions in the image). This is utilized to guide the alignment between the object content codes of the input domain image and object style codes sampled from the style space of the target domain. The style-aligned object representations are further transformed to obtain precise boundaries layout for higher fidelity object generation. The proposed method was systematically compared with different competing methods and obtained significant improvement on both image quality and object recognition performance for translated images.
Couplformer:Rethinking Vision Transformer with Coupling Attention Map

With the development of the self-attention mechanism, the Transformer model has demonstrated its outstanding performance in the computer vision domain. However, the massive computation brought from the full attention mechanism became a heavy burden for memory consumption. Sequentially, the limitation of memory reduces the possibility of improving the Transformer model. To remedy this problem, we propose a novel memory economy attention mechanism named Couplformer, which decouples the attention map into two sub-matrices and generates the alignment scores from spatial information. A series of different scale image classification tasks are applied to evaluate the effectiveness of our model. The result of experiments shows that on the ImageNet-1k classification task, the Couplformer can significantly decrease 28% memory consumption compared with regular Transformer while accessing sufficient accuracy requirements and outperforming 0.92% on Top-1 accuracy while occupying the same memory footprint. As a result, the Couplformer can serve as an efficient backbone in visual tasks, and provide a novel perspective on the attention mechanism for researchers.
Metadata of Linguistic Resources: History and Current State

The main metadata projects for linguistic (language) resources developed over the past 20 years are described. These include the IMDI initiative, the OLAC metadata system. the META-SHARE meta-model, the International Standard Number of Language resources, the evaluation map of language resources, and the CLARIN component metadata model. The content of the ISO metadata standard is described. Projects for creating dictionaries, ontologies, and lexical databases for metadata of language resources are described.
Differentially Private SGD with Sparse Gradients

To protect sensitive training data, differentially private stochastic gradient descent (DP-SGD) has been adopted in deep learning to provide rigorously defined privacy. However, DP-SGD requires the injection of an amount of noise that scales with the number of gradient dimensions, resulting in large performance drops compared to non-private training. In this work, we propose random freeze which randomly freezes a progressively increasing subset of parameters and results in sparse gradient updates while maintaining or increasing accuracy. We theoretically prove the convergence of random freeze and find that random freeze exhibits a signal loss and perturbation moderation trade-off in DP-SGD. Applying random freeze across various DP-SGD frameworks, we maintain accuracy within the same number of iterations while achieving up to 70% representation sparsity, which demonstrates that the trade-off exists in a variety of DP-SGD methods. We further note that random freeze significantly improves accuracy, in particular for large networks. Additionally, axis-aligned sparsity induced by random freeze leads to various advantages for projected DP-SGD or federated learning in terms of computational cost, memory footprint and communication overhead.
Warm education for people's livelihood and well-being: Private enterprises explore the implementation of the Ministry of Education's supply and demand matching employment and education project

In order to promote the organic linkage between employment and training, and the effective connection between supply and demand of talents, the Ministry of Education implemented supply and demand matching employment education project in 2021 for the first time. Aiming at how to make the employment and training of students form an organic linkage as soon as possible, do a good job in the effective connection between the supply and demand of talents, help college students to establish a correct outlook on life, career selection and employment as soon as possible, and help user units to cultivate and recruit more practical, complex According to its own actual situation, Sophia Technology (Shenzhen) Co., Ltd. has carried out the specific practice of the 2021 supply and demand matching employment and education project. This paper communicates and discusses from the aspects of cognitive understanding of the project, grasp of the review process, and data analysis of the project.
Adversarial Deep Reinforcement Learning for Trustworthy Autonomous  Driving Policies

Deep reinforcement learning is widely used to train autonomous cars in a simulated environment. Still, autonomous cars are well known for being vulnerable when exposed to adversarial attacks. This raises the question of whether we can train the adversary as a driving agent for finding failure scenarios in autonomous cars, and then retrain autonomous cars with new adversarial inputs to improve their robustness. In this work, we first train and compare adversarial car policy on two custom reward functions to test the driving control decision of autonomous cars in a multi-agent setting. Second, we verify that adversarial examples can be used not only for finding unwanted autonomous driving behavior, but also for helping autonomous driving cars in improving their deep reinforcement learning policies. By using a high fidelity urban driving simulation environment and vision-based driving agents, we demonstrate that the autonomous cars retrained using the adversary player noticeably increase the performance of their driving policies in terms of reducing collision and offroad steering errors.
Language Models are not Models of Language

Natural Language Processing (NLP) has become one of the leading application areas in the current Artificial Intelligence boom. Transfer learning has enabled large deep learning neural networks trained on the language modeling task to vastly improve performance in almost all language tasks. Interestingly, when the models are trained with data that includes software code, they demonstrate remarkable abilities in generating functioning computer code from natural language specifications. We argue that this creates a conundrum for claims that neural models provide an alternative theory to generative phrase structure grammars in explaining how language works. Since the syntax of programming languages is determined by phrase structure grammars, successful neural models are apparently uninformative about the theoretical foundations of programming languages, and by extension, natural languages. We argue that the term language model is misleading because deep learning models are not theoretical models of language and propose the adoption of corpus model instead, which better reflects the genesis and contents of the model.
Bring Your Mission to Life Through Intentional, Evaluated Online Course Development

The National Teaching & Learning ForumVolume 31, Issue 3 p. 7-9 Online Course Development Bring Your Mission to Life Through Intentional, Evaluated Online Course Development Halley Sutton, Halley SuttonSearch for more papers by this author Halley Sutton, Halley SuttonSearch for more papers by this author First published: 11 March 2022 https://doi.org/10.1002/ntlf.30323Read 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 onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume31, Issue3March 2022Pages 7-9 RelatedInformation
Private quantiles estimation in the presence of atoms

Abstract We consider the differentially private estimation of multiple quantiles (MQ) of a distribution from a dataset, a key building block in modern data analysis. We apply the recent non-smoothed Inverse Sensitivity (IS) mechanism to this specific problem. We establish that the resulting method is closely related to the recently published ad hoc algorithm JointExp. In particular, they share the same computational complexity and a similar efficiency. We prove the statistical consistency of these two algorithms for continuous distributions. Furthermore, we demonstrate both theoretically and empirically that this method suffers from an important lack of performance in the case of peaked distributions, which can degrade up to a potentially catastrophic impact in the presence of atoms. Its smoothed version (i.e. by applying a max kernel to its output density) would solve this problem, but remains an open challenge to implement. As a proxy, we propose a simple and numerically efficient method called Heuristically Smoothed JointExp (HSJointExp), which is endowed with performance guarantees for a broad class of distributions and achieves results that are orders of magnitude better on problematic datasets.
Development of Literature Database of Korean Ritual Studies and Intelligent Platform

The Korean Ritual Studies Series is a compilation of the works about Korean ritual studies, which makes a great contribution to scholarship in this area. Relying on a number of major national programs in China and South Korea, digital collation and research the Korean Ritual Studies Series was made to create into a “literature database of Korean ritual studies” by cooperation between the Center for the Study and Application of Chinese Characters (CSACC) of East China Normal University (ECNU) which is a key research institute of humanities and social science approved by the Ministry of Education of the People’s Republic of China, and the research institutions of Kyungsung University including the Center for the Study of Chinese Characters in Korea, the Research Society of Korean Ritual Studies and the Academy of Korean Studies. The basic framework of the database contains six sub-databases, which are fully correlated with each other according to relevant attributes. The database provides search capability, with three search functions, including catalog index, full-text retrieval and a word index. It is a major achievement of an international cooperation program supported by a multilevel international platform. It provides a large sustainable development space for the “literature database of Korean ritual studies”, which is an indispensable and important part of the “public database of Chinese characters” created by the Center for the Study and Application of Chinese Characters (CSACC) of East China Normal University (ECNU). The three key breakthroughs are needed for its further development: (I) Achieving the data association, comparative research and web publishing of Chinese and Korean ritual literature, establishing an Asian big data ritual research and application platform, supporting the research of Chinese and Korean ritual cultures, the communication history of ritual culture, the development history of Chinese characters and the research of many other disciplines, and meeting the multi-functional needs of ritual research, teaching and communication while completing a comprehensive digital connection of the internal resources of the database. (II) Deeply tapping into the massive Chinese character writing and memory data to create a 21st century database for written texts in the Sinosphere on the basis of digital processing and research of Korean ritual literature. (III) Realizing the multidimensional intelligent automatic recognition and interpretation of original literature written and memorized in Chinese characters such as ancient Chinese and Korean ritual literature, family mottoes and letters of noble families and great clans as well as folk handcopied books, and opening a new era of intelligent interpretation and research of the written texts in the Sinosphere through image recognition technology.
A Magnitude Estimation Approach and its Application to the Yunnan Earthquake Early Warning Network

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Research Letters. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v2]A Magnitude Estimation Approach and its Application to the Yunnan Earthquake Early Warning NetworkAuthorsGuo-quanZHANGiDDanningLIYangGAOSee all authors Guo-quan ZHANGiDCorresponding Author• Submitting AuthorEarthquake Administration of Yunnan ProvinceiDhttps://orcid.org/0000-0002-9468-6132view email addressThe email was not providedcopy email addressDanning LIEarthquake Administration of Yunnan Provinceview email addressThe email was not providedcopy email addressYang GAOEarthquake Administration of Yunnan Provinceview email addressThe email was not providedcopy email address
NEFRET SÖYLEMİ

Enabling people to share their thoughts and actions, World Wide Web (WWW) has created a repository of knowledge and culture. In this sense, differences between humans have been destroyed and everyone in front of the screen has found an opportunity to display themselves as an individual only. The hate speech spreading because of the internet dates back to old times both in Turkey and in the world. A person is brought up in a certain environment with certain rules as of the moment he is born. Later, thoughts and actions that are inappropriate with his own thoughts are transformed into speech most of the time without even realizing it. A speech should include an ideology in order to be considered as a hate speech. These ideologies are transmitted by speeches that are far from peace, aiming an identity or a nation or consisting of all of these. Hate speeches especially increase when a person achieves superiority on the other, a party on another or a country on the other. If hate speeches turn into action, they are punished legally as hate crimes. This study provides a general outlook for the definition, formation and elements of hate speech.
A Data Quarantine Model to Secure Data in Edge Computing

Edge computing provides an agile data processing platform for latency-sensitive and communication-intensive applications through a decentralized cloud and geographically distributed edge nodes. Gaining centralized control over the edge nodes can be challenging due to security issues and threats. Among several security issues, data integrity attacks can lead to inconsistent data and intrude edge data analytics. Further intensification of the attack makes it challenging to mitigate and identify the root cause. Therefore, this paper proposes a new concept of data quarantine model to mitigate data integrity attacks by quarantining intruders. The efficient security solutions in cloud, ad-hoc networks, and computer systems using quarantine have motivated adopting it in edge computing. The data acquisition edge nodes identify the intruders and quarantine all the suspected devices through dimensionality reduction. During quarantine, the proposed concept builds the reputation scores to determine the falsely identified legitimate devices and sanitize their affected data to regain data integrity. As a preliminary investigation, this work identifies an appropriate machine learning method, Linear Discriminant Analysis (LDA), for dimensionality reduction. The LDA results in 72.83% quarantine accuracy and 0.9 seconds training time, which is efficient than other state-of-the-art methods. In future, this would be implemented and validated with ground truth data.