Reviewerly/exHarmony huggingface dataset:
Towards Efficient NLP: A Standard Evaluation and A Strong Baseline

Supersized pre-trained language models have pushed the accuracy of various natural language processing (NLP) tasks to a new stateof-the-art (SOTA).Rather than pursuing the reachless SOTA accuracy, more and more researchers start paying attention to model efficiency and usability.Different from accuracy, the metric for efficiency varies across different studies, making them hard to be fairly compared.To that end, this work presents ELUE (Efficient Language Understanding Evaluation), a standard evaluation, and a public leaderboard for efficient NLP models.ELUE is dedicated to depicting the Pareto Frontier for various language understanding tasks, such that it can tell whether and how much a method achieves Pareto improvement.Along with the benchmark, we also release a strong baseline, ElasticBERT, which allows BERT to exit at any layer in both static and dynamic ways.We demonstrate the ElasticBERT, despite its simplicity, outperforms or performs on par with SOTA compressed and early exiting models.With ElasticBERT, the proposed ELUE has a strong Pareto Frontier and makes a better evaluation for efficient NLP models.
STEAM Practices to Explore Ancient Architectures Using Augmented Reality and 3D Printing with GeoGebra

Abstract In this study, we develop mathematical educational practices for students to explore ancient buildings using GeoGebra, Augmented Reality and 3D printing. It is an interdisciplinary approach, intertwining history, culture, mathematics, and engineering. For example, the 3D modelling of Cheomseongdae in Korea and the Temple of Dendera in Egypt can enable students to practice a multimodal set of traditional and innovative learning approaches. Students might use their mathematical knowledge to reflect on architectural and cultural history in a modeling task.
Integration of micro-services as components in modeling environments for low code development

Low code development environments are gaining attention due to their potential as a development paradigm for very large scale adoption in the future IT. In this paper, we propose a method to extend the (application) Domain Specific Languages supported by two low code development environments based on formal models, namely DIME (native Java) and Pyro (native Python), to include functionalities hosted on heterogeneous technologies and platforms. For this we follow the analogy of micro services. After this integration, both environments can leverage the communication with pre-existing remote RESTful and enterprise systems’ services, in our case Amazon Web Services (AWS) (but this can be easily generalized to other cloud platforms). Developers can this way utilize within DIME and Pyro the potential of sophisticated services, potentially the entire Python and AWS ecosystems, as libraries of drag and drop components in their model driven, low-code style. The new DSLs are made available in DIME and Pyro as collections of implemented SIBs and blocks. Due to the specific capabilities and checks underlying the DIME and Pyro platforms, the individual DSL functionalities are automatically validated for semantic and syntactical errors in both environments.
Erroneous pixel prediction for semantic image segmentation

Abstract We consider semantic image segmentation. Our method is inspired by Bayesian deep learning which improves image segmentation accuracy by modeling the uncertainty of the network output. In contrast to uncertainty, our method directly learns to predict the erroneous pixels of a segmentation network, which is modeled as a binary classification problem. It can speed up training comparing to the Monte Carlo integration often used in Bayesian deep learning. It also allows us to train a branch to correct the labels of erroneous pixels. Our method consists of three stages: (i) predict pixel-wise error probability of the initial result, (ii) redetermine new labels for pixels with high error probability, and (iii) fuse the initial result and the redetermined result with respect to the error probability. We formulate the error-pixel prediction problem as a classification task and employ an error-prediction branch in the network to predict pixel-wise error probabilities. We also introduce a detail branch to focus the training process on the erroneous pixels. We have experimentally validated our method on the Cityscapes and ADE20K datasets. Our model can be easily added to various advanced segmentation networks to improve their performance. Taking DeepLabv3+ as an example, our network can achieve 82.88% of mIoU on Cityscapes testing dataset and 45.73% on ADE20K validation dataset, improving corresponding DeepLabv3+ results by 0.74% and 0.13% respectively.
Comparing the victimization impact of cybercrime and traditional crime

This paper addresses the importance of building knowledge on the impact of cybercrime victimization. Because the topic is understudied, it is unclear whether the impact of cybercrime differs from that of traditional crime. Our understanding of potential impact differences needs to be improved, considering that society and criminality are digitizing and, consequently, more people are likely to become victims of cybercrime. From a practical perspective, knowledge about the impact of different crimes is important to develop victim policies within law enforcement and other relevant agencies, and to treat victims appropriately. In this paper, a literature review is provided, as well as future research directions to address the current knowledge gap. The future research directions are divided in three topics: (1) distinguishing between cybercrime and traditional crime, (2) classifying cybercrime and traditional crime, and (3) measuring the victimization impact of cybercrime and traditional crime.
Institutional Divergence of Digital Item Bank Management in Bureaucratic Hybridization: An Application of SSM Based Multi-Method

This study aims to analyze institutional divergence of Beckert (2010) by measuring the reframing of three constitutive principles of Digital Weberian Bureaucracy (DWB). In contrast to the studies by Gaus et al. (2017), Sofyani et al. (2018), Muellerleile and Robertson (2018), Turner et al. (2019), and Meilani and Hardjosoekarto (2020), this study explores normative and mimetic mechanisms resulting in the mixed pattern of public administration (Traditional Public Administration (TPA), New Public Management (NPM), and Post NPM), focusing on the transformation of Digital Era Governance (DEG). Employing Soft Systems Methodology (SSM) by Reynolds and Holwell (2010), combined with Text Network Analysis (TNA) by Segev (2020) and Social Network Analysis (SNA) by Borgatti et al. (2014), this study shows the micro dynamics of relationships between actors, the meso dynamics of organizations, and the absence of regulations at the macro level, all of which lead to institutional divergence in the form of fully hybrid governance (as proposed by De Waele et al. (2015)) that is also caused by normative and mimetic mechanisms. Complementing the study of DWB, this study suggests that computer literacy and programming languages are essential to be improved by future bureaucrats as social actors to achieve the success of digital transformation.The online version contains supplementary material available at 10.1007/s11213-021-09579-4.
Learning From Web Recipe-Image Pairs for Food Recognition: Problem, Baselines and Performance

Cross-modal recipe retrieval has recently been explored for food recognition and understanding. Text-rich recipe provides not only visual content information (e.g., ingredients, dish presentation) but also procedure of food preparation (cutting and cooking styles). The paired data is leveraged to train deep models to retrieve recipes for food images. Most recipes on the Web include sample pictures as the references. The paired multimedia data is not noise-free, due to errors such as pairing of images containing partially prepared dishes with recipes. The content of recipes and food images are not always consistent due to free-style writing and preparation of food in different environments. As a consequence, the effectiveness of learning cross-modal deep models from such noisy web data is questionable. This paper conducts an empirical study to provide insights whether the features learnt with noisy pair data are resilient and could capture the modality correspondence between visual and text.
Learning Joint Embedding with Modality Alignments for Cross-Modal Retrieval of Recipes and Food Images

This paper presents a three-tier modality alignment approach to learning text-image joint embedding, coined as JEMA, for cross-modal retrieval of cooking recipes and food images. The first tier improves recipe text embedding by optimizing the LSTM networks with term extraction and ranking enhanced sequence patterns, and optimizes the image embedding by combining the ResNeXt-101 image encoder with the category embedding using wideResNet-50 with word2vec. The second tier modality alignment optimizes the textual-visual joint embedding loss function using a double batch-hard triplet loss with soft-margin optimization. The third modality alignment incorporates two types of cross-modality alignments as the auxiliary loss regularizations to further reduce the alignment errors in the joint learning of the two modality-specific embedding functions. The category-based cross-modal alignment aims to align the image category with the recipe category as a loss regularization to the joint embedding. The cross-modal discriminator-based alignment aims to add the visual-textual embedding distribution alignment to further regularize the joint embedding loss. Extensive experiments with the one-million recipes benchmark dataset Recipe1M demonstrate that the proposed JEMA approach outperforms the state-of-the-art cross-modal embedding methods for both image-to-recipe and recipe-to-image retrievals.
Infrared dressing in real time: Emergence of anomalous dimensions

We implement a dynamical resummation method (DRM) as an extension of the dynamical renormalization group to study the time evolution of infrared dressing in nongauge theories. Super renormalizable and renormalizable models feature infrared divergences similar to those of a theory at a critical point, motivating a renormalization group improvement of the propagator that yields a power-law decay of the survival probability $\ensuremath{\propto}{t}^{\ensuremath{-}\mathrm{\ensuremath{\Delta}}}$. The DRM confirms this decay, yields the dressed state, and determines that the anomalous dimension $\mathrm{\ensuremath{\Delta}}$ is completely determined by the slope of the spectral density at threshold independent of the ultraviolet behavior, suggesting certain universality for infrared phenomena. The dressed state is an entangled state of the charged and massless quanta. The entanglement entropy is obtained by tracing over the unobserved massless quanta. Its time evolution is determined by the DRM, is infrared finite, and describes the information flow from the initial single particle to the asymptotic multiparticle dressed state. We show that effective field theories of massless axionlike particles coupled to fermion fields do not feature infrared divergences and provide a criterion for infrared divergences in effective field theories valid for nongauge theories up to one loop.
Using new artificial bee colony as probabilistic neural network for breast cancer data classification

Purpose Breast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications. Design/methodology/approach The new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast cancer data set. Findings The new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming. Originality/value The new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification.
Phase-state duality in reversible circuit design

The reversible implementation of classical functions accounts for the bulk of most known quantum algorithms. As a result, a number of reversible circuit constructions over the Clifford+$T$ gate set have been developed in recent years which use both the state and phase spaces, or $X$ and $Z$ bases, to reduce circuit costs beyond what is possible at the strictly classical level. We study and generalize two particular classes of these constructions: relative phase circuits, including Giles and Selinger's multiply-controlled $iX$ gates and Maslov's $4$ qubit Toffoli gate, and measurement-assisted circuits, including Jones' Toffoli gate and Gidney's temporary logical-AND. In doing so, we introduce general methods for implementing classical functions up to phase and for measurement-assisted termination of temporary values. We then apply these techniques to find novel $T$-count efficient constructions of some classical functions in space-constrained regimes, notably multiply-controlled Toffoli gates and temporary products.
Functional encryption for set intersection in the multi-client setting

Functional encryption for set intersection (FE-SI) in the multi-client environment is that each client i encrypts a set Xi associated with time T by using its own encryption key and uploads it to a cloud server, and then the cloud server which receives a function key of the client indexes i, j from a trusted center can compute the intersection Xi∩Xj of the two client ciphertexts. In this paper, we first newly define the concept of FE-SI suitable for the multi-client setting. Then, we propose an efficient FE-SI scheme in asymmetric bilinear groups and prove the static security of our scheme under newly introduced assumptions. In our FE-SI scheme, a ciphertext consists of O(ℓ) group elements, a function key consists of a single group element, and the decryption algorithm has O(ℓ2) complexity where ℓ is the size of a set in the ciphertext. Next, we propose another FE-SI scheme with time-constrained keys that limits the ability of function keys to be valid only for a specified time period T, and proves the static security of our scheme. Finally, we prove that the two assumptions hold in the general group model to provide confidence in the two newly introduced assumptions.
3D reconstruction of structured light based on infrared MEMS

The traditional 3D reconstruction process of structured light is based on a DLP visible light grating projection method; the DLP method has many flaws, including great bulk, and harmful to human eyes caused by visible light and so on. In this article, we propose a method based on infrared micro electro mechanical systems (MEMS) instead of DLP to generate programmable coded structured light. First, MEMS emits a laser beam by using a point-beam laser. Then, the laser beam is modulated by the optical system and transformed into a laser line, which is reflected by MEMS and projected onto the object to be measured. The grating pattern can be a square wave or a sinusoidal wave for different needs. Due to the speckle characteristics of the infrared laser, the phase error obtained by the traditional four-step phase-shifting method is very large. Therefore, an eight-step phase-shifting method based on the Gaussian filter is proposed to improve the decoding accuracy of the grating. An improved calibration method based on phase space is used to calibrate the monocular system. Finally, the 3D data of the object are obtained according to this method, and the 3D reconstruction accuracy is better than 0.1 mm.
The double-edged sword of AI: Ethical Adversarial Attacks to counter artificial intelligence for crime

Abstract Artificial intelligence (AI) has found a myriad of applications in many domains of technology, and more importantly, in improving people’s lives. Sadly, AI solutions have already been utilized for various violations and theft, even receiving the name AI or Crime (AIC). This poses a challenge: are cybersecurity experts thus justified to attack malicious AI algorithms, methods and systems as well, to stop them? Would that be fair and ethical? Furthermore, AI and machine learning algorithms are prone to be fooled or misled by the so-called adversarial attacks. However, adversarial attacks could be used by cybersecurity experts to stop the criminals using AI, and tamper with their systems. The paper argues that this kind of attacks could be named Ethical Adversarial Attacks (EAA), and if used fairly, within the regulations and legal frameworks, they would prove to be a valuable aid in the fight against cybercrime.
The consensus number of a cryptocurrency

Many blockchain-based algorithms, such as Bitcoin, implement a decentralized asset transfer system, often referred to as a cryptocurrency. As stated in the original paper by Nakamoto, at the heart of these systems lies the problem of preventing double-spending; this is usually solved by achieving consensus on the order of transfers among the participants. In this paper, we treat the asset transfer problem as a concurrent object and determine its consensus number, showing that consensus is, in fact, not necessary to prevent double-spending. We first consider the problem as defined by Nakamoto, where only a single process-the account owner-can withdraw from each account. Safety and liveness need to be ensured for correct account owners, whereas misbehaving account owners might be unable to perform transfers. We show that the consensus number of an asset transfer object is 1. We then consider a more general k-shared asset transfer object where up to k processes can atomically withdraw from the same account, and show that this object has consensus number k. We establish our results in the context of shared memory with benign faults, allowing us to properly understand the level of difficulty of the asset transfer problem. We also translate these results in the message passing setting with Byzantine players, a model that is more relevant in practice. In this model, we describe an asynchronous Byzantine fault-tolerant asset transfer implementation that is both simpler and more efficient than state-of-the-art consensus-based solutions. Our results are applicable to both the permissioned (private) and permissionless (public) setting, as normally their differentiation is hidden by the abstractions on top of which our algorithms are based.
Analysis of Application Examples of Differential Privacy in Deep Learning

Artificial Intelligence has been widely applied today, and the subsequent privacy leakage problems have also been paid attention to. Attacks such as model inference attacks on deep neural networks can easily extract user information from neural networks. Therefore, it is necessary to protect privacy in deep learning. Differential privacy, as a popular topic in privacy-preserving in recent years, which provides rigorous privacy guarantee, can also be used to preserve privacy in deep learning. Although many articles have proposed different methods to combine differential privacy and deep learning, there are no comprehensive papers to analyze and compare the differences and connections between these technologies. For this purpose, this paper is proposed to compare different differential private methods in deep learning. We comparatively analyze and classify several deep learning models under differential privacy. Meanwhile, we also pay attention to the application of differential privacy in Generative Adversarial Networks (GANs), comparing and analyzing these models. Finally, we summarize the application of differential privacy in deep neural networks.
Cross-Disciplinary Code Switching as Means of Encouraging Creativity

The prerequisites of the study were the contradiction revealed in the teaching process between the need to form a set of professional competencies that combine different disciplines and their perception by students. The study angle involved economics, mathematics and English - the underlying subjects that are basic for an economist. The purpose of the publication is to develop a particular methodology for encouraging creativity in solving applied problems, developing economists and financiers’ professional skills based on interdisciplinary code switching: economics - mathematics – IT - English. Research methods include the study of scientific publications on code-switching theory, pedagogical design and testing created resources. The paper presents a technique for forming a sequence of switching codes, that combine Mathematics, Economics, and English and describe the digital resources created on their basis: a glossary of economic terms and a thesaurus of the R programming language for the discipline “Digital Mathematics”. The authors’ testing of first-year students and their solution of applied problems to follow shows that the application of interdisciplinary code switching on the basis of specially organized language support contributes to the formation of more stable creative thinking and IT-skills. The novelty of the research lies in the development of the theory of code switching by the authors and the formation of a methodology for its use via interdisciplinary interaction.
Problem-oriented Teaching Mode based on Data Mining Technology in the Sports Psychology Classroom

Sports psychology is a branch of psychology and is a sports course that takes both theory and practice into account. It is not only an integral part of the talent training curriculum system of sports colleges and universities, but also a basic compulsory course for social sports majors in comprehensive universities. Sports psychology has currently become one of the significant theories in PE teaching, which not only guides PE teaching, but also helps solve existing problems in PE teaching and drives the development of PE teaching. Subject to the impact of traditional education mode and educational concept, etc., however, there are some problems in the existing the teaching of sports psychology, such as students’ low interest, poor initiative and poor educational effect. To raise students’ learning interest, this paper selected a problem-oriented modular teaching mode, and applied it to the sports psychology classroom, to improve the learning effect. In this study, we first used data mining technology to build an anxiety prediction model and then identified a problem to be solved. After that, the problem was designed and analyzed, the known problem was confirmed, new information was collected, old and new knowledge was integrated, until the problem was finally solved. On this basis, based on virtual simulation technology, an experimental simulation teaching system for sports psychology was presented. It was found through research that the problems-oriented teaching mode can significantly enhance the effect of sports psychology and PE teaching and increase students’ learning initiative. The learning effect was significantly better than that of traditional teaching methods.